Large Language Models (LLMs) have emerged as powerful tools for generating content and facilitating information seeking across diverse domains. While their integration into conversational systems opens new avenues for interactive information-seeking experiences, their effectiveness is constrained by their knowledge boundaries-the limits of what they know and their ability to provide reliable, truthful, and contextually appropriate information. Understanding these boundaries is essential for maximizing the utility of LLMs for real-time information seeking while ensuring their reliability and trustworthiness. In this tutorial, we will explore the taxonomy of knowledge boundary in LLMs, addressing their handling of uncertainty, response calibration, and mitigation of unintended behaviors that can arise during interaction with users. We will also present advanced techniques for optimizing LLM behavior in generative information-seeking tasks, ensuring that models align with user expectations of accuracy and transparency. Attendees will gain insights into research trends and practical methods for enhancing the reliability and utility of LLMs for trustworthy information access.

Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior—a crucial aspect of intelligent conversations—is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains.

In mixed-initiative conversational search systems, clarifying questions aid users who struggle to express their intentions in a single query. These questions aim to uncover user's information needs and resolve query ambiguities. We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. Therefore, we propose to add images to clarifying questions and formulate the novel task of asking multimodal clarifying questions in open-domain, mixed-initiative conversational search systems. To facilitate research into this task, we collect a dataset named Melon that contains over 4k multimodal clarifying questions, enriched with over 14k images. We also propose a multimodal query clarification model named Marto and adopt a prompt-based, generative fine-tuning strategy to perform the training of different stages with different prompts. Several analyses are conducted to understand the importance of multimodal contents during the query clarification phase. Experimental results indicate that the addition of images leads to significant improvements of up to 90% in retrieval performance when selecting the relevant images. Extensive analyses are also performed to show the superiority of Marto compared with discriminative baselines.

Asking clarifying questions has become a key element of various conversational systems, allowing for an effective resolution of ambiguity and uncertainty through natural language questions. Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. Specifically, we first align the representation space between textual and spatial information by encoding spatial states with textual descriptions. Then a multi-relational graph is constructed to capture the spatial relations and enable spatial reasoning with relational graph attention networks. Finally, a unified encoder is adopted to fuse the multimodal information for asking clarification questions. Experimental results on the latest IGLU dataset show the superiority of the proposed method over existing approaches.

Conversational recommender systems (CRS) endow traditional recommender systems with the capability of dynamically obtaining users’ short-term preferences for items and attributes through interactive dialogues. There are three core challenges for CRS, including the intelligent decisions for what attributes to ask, which items to recommend, and when to ask or recommend, at each conversation turn. Previous methods mainly leverage reinforcement learning (RL) to learn conversational recommendation policies for solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components. These approaches restrict the scalability and generality of CRS and fall short of preserving a stable training procedure. In the light of these challenges, we tackle these three decision-making problems in CRS as a unified policy learning task. In order to leverage different features that are important to each sub-problem and facilitate better unified policy learning in CRS, we propose two novel multi-agent RL-based frameworks, namely Independent and Hierarchical Multi-Agent UNIfied COnversational RecommeNders (IMA-UNICORN and HMA-UNICORN), respectively. In specific, two low-level agents enrich the state representations for attribute prediction and item recommendation, by combining the long-term user preference information from the historical interaction data and the short-term user preference information from the conversation history. A high-level meta agent is responsible for coordinating the low-level agents to adaptively make the final decision. Experimental results on four benchmark CRS datasets and a real-world E-Commerce application show that the proposed frameworks significantly outperform state-of-the-art methods. Extensive analyses further demonstrate the superior scalability of the MARL frameworks on the multi-round conversational recommendation.

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users’ interests and naturally lead user-engaged dialogues with multiple conversational goals and diverse topics. Four tasks are often involved in MG-CRS, including Goal Planning, Topic Prediction, Item Recommendation, and Response Generation. Most existing studies address only some of these tasks. To handle the whole problem of MG-CRS, modularized frameworks are adopted where each task is tackled independently without considering their interdependencies. In this work, we propose a novel Unified MultI-goal conversational recommeNDer system (UniMIND). Specifically, we unify these four tasks with different formulations into the same sequence-to-sequence paradigm. Prompt-based learning strategies are investigated to endow the unified model with the capability of multi-task learning. Finally, the overall learning and inference procedure consists of three stages, including multi-task learning, prompt-based tuning, and inference. Experimental results on two MG-CRS benchmarks (DuRecDial and TG-ReDial) show that UniMIND achieves state-of-the-art performance on all tasks with a unified model. Extensive analyses and discussions are provided for shedding some new perspectives for MG-CRS.

The role of social media in fashion industry has been blooming as the years have continued on. In this work, we investigate sentiment analysis for fashion related posts in social media platforms. There are two main challenges of this task. On the first place, information of different modalities must be jointly considered to make the final predictions. On the second place, some unique fashion related attributes should be taken into account. While most existing works focus on traditional multimodal sentiment analysis, they always fail to exploit the fashion related attributes in this task. We propose a novel framework that jointly leverages the image vision, post text, as well as fashion attribute modality to determine the sentiment category. One characteristic of our model is that it extracts fashion attributes and integrates them with the image vision information for effective representation. Furthermore, it exploits the mutual relationship between the fashion attributes and the post texts via a mutual attention mechanism. Since there is no existing dataset for this task, we prepare a large-scale sentiment analysis dataset of over 12k fashion related social media posts. Extensive experiments are conducted to demonstrate the effectiveness of our model.

User Satisfaction Estimation (USE) is an important yet challenging task in goal-oriented conversational systems. Whether the user is satisfied with the system largely depends on the fulfillment of the user’s needs, which can be implicitly reflected by users’ dialogue acts. However, existing studies often neglect the sequential transitions of dialogue act or rely heavily on annotated dialogue act labels when utilizing dialogue acts to facilitate USE. In this paper, we propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction, by jointly learning User Satisfaction Estimation and Dialogue Act Recognition tasks. In specific, we first employ a Hierarchical Transformer to encode the whole dialogue context, with two task-adaptive pre-training strategies to be a second-phase in-domain pre-training for enhancing the dialogue modeling ability. In terms of the availability of dialogue act labels, we further develop two variants of USDA to capture the dialogue act information in either supervised or unsupervised manners. Finally, USDA leverages the sequential transitions of both content and act features in the dialogue to predict the user satisfaction. Experimental results on four benchmark goal-oriented dialogue datasets across different applications show that the proposed method substantially and consistently outperforms existing methods on USE, and validate the important role of dialogue act sequences in USE.

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same “completely summarized” answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences toward product aspects or information needs. To tackle this challenge, we propose a novel Personalized Answer GEneration method with multi-perspective preference modeling, which explores historical user-generated contents to model user preference for generating personalized answers in PQA. Specifically, we first retrieve question-related user history as external knowledge to model knowledge-level user preference. Then, we leverage the Gaussian Softmax distribution model to capture latent aspect-level user preference. Finally, we develop a persona-aware pointer network to generate personalized answers in terms of both content and style by utilizing personal user preference and dynamic user vocabulary. Experimental results on real-world E-Commerce QA datasets demonstrate that the proposed method outperforms existing methods by generating informative and customized answers and show that answer generation in E-Commerce can benefit from personalization.

Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware attention mechanism to summarize both the context-based and knowledge-based interactions between questions and answers. To handle the diversity and complexity of KG information, we further propose a Contextualized Knowledge-aware Attentive Neural Network, which improves the knowledge representation learning with structure information via a customized Graph Convolutional Network and comprehensively learns context-based and knowledge-based sentence representation via the multi-view knowledge-aware attention mechanism. We evaluate our method on four widely used benchmark QA datasets, including WikiQA, TREC QA, InsuranceQA, and Yahoo QA. Results verify the benefits of incorporating external knowledge from KG and show the robust superiority and extensive applicability of our method.

Conversational recommender systems (CRS) enable the traditional recommender systems to explicitly acquire user preferences towards items and attributes through interactive conversations. Reinforcement learning (RL) is widely adopted to learn conversational recommendation policies to decide what attributes to ask, which items to recommend, and when to ask or recommend, at each conversation turn. However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure. In the light of these challenges, we propose to formulate these three decision-making problems in CRS as a unified policy learning task. In order to systematically integrate conversation and recommendation components, we develop a dynamic weighted graph based RL method to learn a policy to select the action at each conversation turn, either asking an attribute or recommending items. Further, to deal with the sample efficiency issue, we propose two action selection strategies for reducing the candidate action space according to the preference and entropy information. Experimental results on two benchmark CRS datasets and a real-world E-Commerce application show that the proposed method not only significantly outperforms state-of-the-art methods but also enhances the scalability and stability of CRS.

We study the task of conversational fashion image retrieval via multiturn natural language feedback. Most previous studies are based on single-turn settings. Existing models on multiturn conversational fashion image retrieval have limitations, such as employing traditional models, and leading to ineffective performance. We propose a novel framework that can effectively handle conversational fashion image retrieval with multiturn natural language feedback texts. One characteristic of the framework is that it searches for candidate images based on exploitation of the encoded reference image and feedback text information together with the conversation history. Furthermore, the image fashion attribute information is leveraged via a mutual attention strategy. Since there is no existing fashion dataset suitable for the multiturn setting of our task, we derive a large-scale multiturn fashion dataset via additional manual annotation efforts on an existing single-turn dataset. The experiments show that our proposed model significantly outperforms existing state-of-the-art methods.

Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question. In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model. Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process. Then a multi-view pointer-generator network is employed to generate opinion-aware answers for a given product-related question. Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets, and effectively generate opinionated and informative answers.

Non-factoid question answering (QA) is one of the most extensive yet challenging application and research areas of retrieval-based question answering. In particular, answers to non-factoid questions can often be too lengthy and redundant to comprehend, which leads to the great demand on answer sumamrization in non-factoid QA. However, the multi-level interactions between QA pairs and the interrelation among different answer sentences are usually modeled separately on current answer summarization studies. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. Specifically, we design a hierarchical compare-aggregate method to integrate the interaction between QA pairs in both word-level and sentence-level into the final question and answer representations. After that, we conduct the question-aware sequential extractor to produce a summary for the lengthy answer. Experimental results show that answer summarization benefits from both hierarchical and sequential context modeling and our method achieves superior performance on WikiHowQA and PubMedQA.

Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking. We propose an answer ranking model named MUSE which carefully models multiple semantic relations among the question, answers, and relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph with the question, each answer, and each review snippet as nodes. Then a customized graph convolutional neural network is designed for explicitly modeling the semantic relevance between the question and answers, the content consistency among answers, and the textual entailment between answers and reviews. Extensive experiments on real-world E-commerce datasets across three product categories show that our proposed model achieves superior performance on the concerned answer ranking task.

Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing. Accurately predicting the helpfulness of an answer to a given question and thus identifying helpful answers is becoming a demanding need. Since the helpfulness of an answer depends on multiple perspectives instead of only topical relevance investigated in typical QA tasks, common answer selection algorithms are insufficient for tackling this task. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds’ opinions reflected in the reviews, which is another important factor to identify helpful answers. Moreover, we tackle the task of determining opinion coherence as a language inference problem and explore the utilization of pre-training strategy to transfer the textual inference knowledge obtained from a specifically designed trained network. Extensive experiments conducted on real-world data across seven product categories show that our proposed model achieves superior performance on the prediction task.

Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the “persona” information which captures the intrinsic language style of the users or the different characteristics of the product items. In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items. The latent variables from aVAE are regarded as persona embeddings. Besides representing persona using the latent embeddings, we design a persona memory for storing the persona related words for users and items. Pointer Network is used to retrieve persona wordings from the memory when generating tips. Moreover, the persona embeddings are used as latent factors by a rating prediction component to predict the sentiment of a user over an item. Finally, the persona embeddings and the sentiment information are incorporated into a recurrent neural networks based tips generation component. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.

When a significant event occurs, many news articles from different newsagents often report it. Moreover, these newsagents also provide platforms for their readers to write comments expressing their views or understanding. Through digesting these reader comments, we can gain insights into the reactions, suggestions, personal experiences, or public opinions with respect to the emerging event. However, these reader comments from different sources are often rapidly accumulated resulting in an enormous volume. It becomes difficult to manually analyze these comments. In this paper, we propose a framework that can digest reader comments automatically through latent event facets and news specificity. An event facet refers to the aspect of the event concerned by many readers. Specifically, some of the reader comments, despite coming from different sources, discuss a certain facet of the event. Such facets provide an effective means for organizing news comments in a global manner. On the other hand, some comments discuss the specific topic of the corresponding news article. These specific topics demonstrate the specific focus of readers on the piece of news locally. Such reader comment digest in different granularities facilitates readers deeper understanding of these enormous comments. To achieve the above desirable goal of digesting reader comments, we propose an unsupervised model called EFNS which is capable of capturing the intricate fine-grained associations among events, news, and comments. We also develop a multiplicative-update method to infer the parameters and prove the convergence of our algorithm. Our framework can also visualize reader comments according to the relationship with latent event facets and the degree of news specificity. Experimental results show that our proposed EFNS model can provide an effective way to digest news reader comments and outperform the state-of-the-art method.

In E-commerce sites, there are platforms for users to pose product-related questions and experienced customers may provide answers voluntarily. Among the questions asked by users, a large proportion of them are yes-no questions reflecting that users wish to know whether or not the product can satisfy a certain criterion or meet a certain expectation. Both Question Answering (QA) approaches and Community Question Answering methods are not suitable for answer prediction for new questions in this setting. The reasons are that questions are product-associated and many of them are concerned about user experiences and subjective opinions. In addition to existing question-answer pairs, user written reviews can provide useful clues for answer prediction. In this paper, we propose a new framework that can tackle the task of review-aware answer prediction for product-related questions. The aspect analytics model in this framework learns latent aspects as well as aspect-specific embeddings of reviews via a 3-order Autoencoder. One advantage of this learned model is that it can generate aspect-specific representations for new questions. The predictive answer model in our framework, learned jointly from existing questions, answers, and reviews, is able to predict the answers for new yes-no questions taking into consideration of aspects. Besides, our framework can provide supportive reviews grouped by relevant aspects serving as information for explainable answers. Experiment results on 15 different product categories from a large-scale benchmark E-commence QA dataset demonstrate the effectiveness of our framework.

Event-based social networks (EBSNs) are becoming popular in recent years. Users can publish a planned event on an EBSN website, calling for other users to participate in the event. When a user is making a decision on whether to participate in an event in EBSNs, one aspect for consideration is existing participants defined as users who have agreed to join this event. Existing participants of the event may affect the decision of the user, to which we refer as participant influence. However, participant influence is not well studied by previous works. In this article, we propose an event recommendation model that considers participant influence, and exploits the influence of existing participants on the decisions of new participants based on Poisson factorization. The effect of participant influence is associated with the target event, the host group of the event, and the location of the event. Furthermore, our proposed model can extract latent event topics from event text descriptions, and characterize events, groups, and locations by distributions of event topics. Associations between latent event topics and participant influence are exploited for improving event recommendation. Besides making event recommendation, the proposed model is able to reveal the semantic properties of the participant influence between two users semantically. We have conducted extensive experiments on some datasets extracted from a real-world EBSN. Our proposed model achieves superior event recommendation performance over several state-of-the-art models. The results demonstrate that the consideration of participant influence can improve event recommendation.

Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate'' user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.

Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a more global view, looking at the word distributions across the corpus to assign a topic to each word occurrence. These two paradigms are complementary in how they represent the meaning of word occurrences. While some previous works have already looked at using word embeddings for improving the quality of latent topics, and conversely, at using latent topics for improving word embeddings, such "two-step'' methods cannot capture the mutual interaction between the two paradigms. In this paper, we propose STE, a framework which can learn word embeddings and latent topics in a unified manner. STE naturally obtains topic-specific word embeddings, and thus addresses the issue of polysemy. At the same time, it also learns the term distributions of the topics, and the topic distributions of the documents. Our experimental results demonstrate that the STE model can indeed generate useful topic-specific word embeddings and coherent latent topics in an effective and efficient way.

Event-based Social Networks (EBSN) are popular for organizing offline social events nowadays. In this paper, we develop a new model for event recommendation on EBSNs, which exploits the influence of existing participants, who have expressed willingness to join, on new participants in addition to other context information. Utilizing the participant influence can improve the effectiveness of event recommendation. Experiments on real datasets confirm that the consideration of participant influence can lead to more accurate prediction, offering better event recommendation.

News articles from different sources reporting the same event are often associated with an enormous amount of reader comments resulting in difficulty in digesting the comments manually. Some of these comments, despite coming from different sources, discuss about a certain facet of the event. On the other hand, some comments discuss on the specific topic of the corresponding news article. We propose a framework that can digest reader comments automatically via fine-grained associations with event facets and news. We propose an unsupervised model called DRC, based on collective matrix factorization and develop a multiplicative-update method to infer the parameters. Experimental results show that our proposed DRC model can provide an effective way to digest news reader comments.

Many news websites from different regions in the world allow readers to write comments in their own languages about an event. Digesting such enormous amount of comments in different languages is difficult. One elegant way to digest and organize these comments is to detect latent discussion topics with the consideration of language attributes. Some discussion topics are common topics shared between languages whereas some topics are specifically dominated by a particular language. To tackle this task of discovering discussion topics that exhibit commonality or specificity from news reader comments written in different languages, we propose a new model called TDCS based on graphical models, which can cope with the language gap and detect language-common and language-specific latent discussion topics simultaneously. Our TDCS model also exploits comment-oriented clues via a scalable Dirichlet Multinomial Regression method. To learn the model parameters, we develop an inference method which alternates between EM and Gibbs sampling. Experimental results show that our proposed TDCS model can provide an effective way to digest multilingual news reader comments.

When a user is making a decision on whether to participate an event in Event-based Social Networks (EBSN), one of the common considerations is who have agreed to join this event. The reason is that existing participants of the event affect the decision of the user, to which we refer as participant influence. However, participant influence is not well studied by previous works. In this paper, we propose an event recommendation model which considers participant influence, exploiting the influence of existing participants, on the decisions of new participants. Specifically, we investigate participant influence in relation to several commonly used contextual aspects of the event based on Poisson factorization. We have conducted extensive experiments on some datasets extracted from a real-world EBSN. The results demonstrate that the consideration of participant influence can improve event recommendation.

While most methods for learning-to-rank documents only consider relevance scores as features, better results can often be obtained by taking into account the latent topic structure of the document collection. Existing approaches that consider latent topics follow a two-stage approach, in which topics are discovered in an unsupervised way, as usual, and then used as features for the learning-to-rank task. In contrast, we propose a learning-to-rank framework which integrates the supervised learning of a maximum margin classifier with the discovery of a suitable probabilistic topic model. In this way, the labelled data that is available for the learning-to-rank task can be exploited to identify the most appropriate topics. To this end, we use a unified constrained optimization framework, which can dynamically compute the latent topic similarity score between the query and the document. Our experimental results show a consistent improvement over the state-of-the-art learning-to-rank models.

An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data.

Most collaborative filtering (CF) algorithms only make use of the rating scores given by users for items. However, it is often the case that each rating score is associated with a piece of review text. Such review texts, which are capable of providing us valuable information to reveal the reasons why users give a certain rating, have not been exploited and they are usually ignored by most CF algorithms. Moreover, the underlying relationship buried in users and items has not been fully exploited. Items we would recommend can often be characterized into hidden groups (e.g. comedy, horror movie and action movie), and users can also be organized as hidden communities. We propose a new generative model to predict user's ratings on previously unrated items by considering review texts as well as hidden user communities and item groups relationship. Regarding the rating scores, traditional algorithms would not perform well on uncovering the community and group information of each user and each item since the user-item rating matrix is dyadic involving the mutual interactions between users and items. Instead, co-clustering, which is capable of conducting simultaneous clustering of two variables, is able to take advantage of such user-item relationships to better predict the rating scores. Additionally, co-clustering would be more effective for modeling the generation of review texts since different user communities would discuss different topics and vary their own wordings or expression patterns when dealing with different item groups. Besides, by modeling as a mixed membership over community and group respectively, each user or item can belong to multiple communities or groups with varying degrees. We have conducted extensive experiments to predict the missing rating scores on 22 real word datasets. The experimental results demonstrate the superior performance of our proposed model comparing with the state-of-the-art methods.

We investigate latent aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in a domain in an unsupervised manner. One goal is to discover a set of aspects which are previously unknown for the domain, and predict the user's ratings on each aspect for each review. Another goal is to detect key terms for each aspect. Existing works on predicting aspect ratings fail to handle the aspect sparsity problem in the review texts leading to unreliable prediction. We propose a new generative model to tackle the latent aspect mining problem in an unsupervised manner. By considering the user and item side information of review texts, we introduce two latent variables, namely, user intrinsic aspect interest and item intrinsic aspect quality facilitating better modeling of aspect generation leading to improvement on the accuracy and reliability of predicted aspect ratings. Furthermore, we provide an analytical investigation on the Maximum A Posterior (MAP) optimization problem used in our proposed model and develop a new block coordinate gradient descent algorithm to efficiently solve the optimization with closed-form updating formulas. We also study its convergence analysis. Experimental results on the two real-world product review corpora demonstrate that our proposed model outperforms existing state-of-the-art models.

We propose a framework which can perform Web page segmentation with a structured prediction approach. It formulates the segmentation task as a structured labeling problem on a transformed Web page segmentation graph (WPS-graph). WPS-graph models the candidate segmentation boundaries of a page and the dependency relation among the adjacent segmentation boundaries. Each labeling scheme on the WPS-graph corresponds to a possible segmentation of the page. The task of finding the optimal labeling of the WPS-graph is transformed into a binary Integer Linear Programming problem, which considers the entire WPS-graph as a whole to conduct structured prediction. A learning algorithm based on the structured output Support Vector Machine framework is developed to determine the feature weights, which is capable to consider the inter-dependency among candidate segmentation boundaries. Furthermore, we investigate its efficacy in supporting the development of automatic Web page classification.

We investigate the problem of general entity retrieval for enterprise websites. Our framework transforms the webpage content into a structured content representation, which captures hierarchical information blocks and semi-structured data records information. To facilitate entity retrieval given a user query, we develop a structured positional entity language model suitable for ranking entities extracted from the webpage content incorporating the structured content representation. Different from existing language models for retrieval, our proposed model considers both the proximity and the structured webpage content in a unified manner. Extensive experiments on the benchmark datasets demonstrate the effectiveness of our proposed framework.

We present a new unsupervised topic discovery model for a collection of text documents. In contrast to the majority of the state-of-the-art topic models, our model does not break the document's structure such as paragraphs and sentences. In addition, it preserves word order in the document. As a result, it can generate two levels of topics of different granularity, namely, segment-topics and word-topics. In addition, it can generate n-gram words in each topic. We also develop an approximate inference scheme using Gibbs sampling method. We conduct extensive experiments using publicly available data from different collections and show that our model improves the quality of several text mining tasks such as the ability to support fine grained topics with n-gram words in the correlation graph, the ability to segment a document into topically coherent sections, document classification, and document likelihood estimation.

We develop a new framework to achieve the goal of Wikipedia entity expansion and attribute extraction from the Web. Our framework takes a few existing entities that are automatically collected from a particular Wikipedia category as seed input and explores their attribute infoboxes to obtain clues for the discovery of more entities for this category and the attribute content of the newly discovered entities. One characteristic of our framework is to conduct discovery and extraction from desirable semi-structured data record sets which are automatically collected from the Web. A semi-supervised learning model with Conditional Random Fields is developed to deal with the issues of extraction learning and limited number of labeled examples derived from the seed entities. We make use of a proximate record graph to guide the semi-supervised learning process. The graph captures alignment similarity among data records. Then the semi-supervised learning process can leverage the unlabeled data in the record set by controlling the label regularization under the guidance of the proximate record graph. Extensive experiments on different domains have been conducted to demonstrate its superiority for discovering new entities and extracting attribute content.

Web data record extraction aims at extracting a set of similar object records from a single webpage. These records have similar attributes or fields and are presented with a regular format in a coherent region of the page. To tackle this problem, most existing works analyze the DOM tree of an input page. One major limitation of these methods is that the lack of a global view in detecting data records from an input page results in a myopic decision. Their brute-force searching manner in detecting various types of records degrades the flexibility and robustness. We propose a Structure-Knowledge-Oriented Global Analysis (Skoga) framework which can perform robust detection of different-kinds of data records and record regions. The major component of the Skoga framework is a DOM structure-knowledge-driven detection model which can conduct a global analysis on the DOM structure to achieve effective detection. The DOM structure knowledge consists of background knowledge as well as statistical knowledge capturing different characteristics of data records and record regions, as exhibited in the DOM structure. The background knowledge encodes the semantics of labels indicating general constituents of data records and regions. The statistical knowledge is represented by some carefully designed features that capture different characteristics of a single node or a node group in the DOM. The feature weights are determined using a development dataset via a parameter estimation algorithm based on a structured output support vector machine. An optimization method based on the divide-and-conquer principle is developed making use of the DOM structure knowledge to quantitatively infer and recognize appropriate records and regions for a page. Extensive experiments have been conducted on four datasets. The experimental results demonstrate that our framework achieves higher accuracy compared with state-of-the-art methods.

We propose a novel framework for determining the conceptual difficulty of a domain-specific text document without using any external lexicon. Conceptual difficulty relates to finding the reading difficulty of domain-specific documents. Previous approaches to tackling domain-specific readability problem have heavily relied upon an external lexicon, which limits the scalability to other domains. Our model can be readily applied in domain-specific vertical search engines to re-rank documents according to their conceptual difficulty. We develop an unsupervised and principled approach for computing a term's conceptual difficulty in the latent space. Our approach also considers transitions between the segments generated in sequence. It performs better than the current state-of-the-art comparative methods.

Although the task of data record extraction from Web pages has been studied extensively, yet it fails to handle many pages due to their complexity in format or layout. In this paper, we propose a unified method to tackle this task by addressing several key issues in a uniform manner. A new search structure, named as Record Segmentation Tree (RST), is designed, and several efficient search pruning strategies on the RST structure are proposed to identify the records in a given Web page. Another characteristic of our method which is significantly different from previous works is that it can effectively handle complicated and challenging data record regions. It is achieved by generating subtree groups dynamically from the RST structure during the search process. Furthermore, instead of using string edit distance or tree edit distance, we propose a token-based edit distance which takes each DOM node as a basic unit in the cost calculation. Extensive experiments are conducted on four data sets, including flat, nested, and intertwine records. The experimental results demonstrate that our method achieves higher accuracy compared with three state-of-the-art methods.

An important way to improve users' satisfaction in Web search is to assist them to issue more effective queries. One such approach is query refinement (reformulation), which generates new queries according to the current query issued by users. A common procedure for conducting refinement is to generate some candidate queries first, and then a scoring method is designed to assess the quality of these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the historical queries, and cannot detect and maintain the semantic consistency of queries. In this paper, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to assess the semantic dependency of terms in a query. In the graphical model, both term context dependency and topic context dependency are considered. This also makes it feasible to score some queries which do not have much available historical term context information. We also utilize social tagging data in the candidate query generation process. Based on the observation that different users may tag the same resource with different tags of similar meaning, we propose a method to mine these term pairs for new candidate query construction.

Users look for information that can suit their level of expertise, but it often takes a mammoth effort to trace such information. One has to sift through multiple pages to look for one that fits the appropriate technical background. In this paper, a query-independent ranking system is proposed for technical web pages. The pages returned by the system are sorted by their relative technical difficulty in either ascending or descending order specified by the user. The technical difficulty of a document i.e. terms in sequence, is first computed by the combination of each individual term's geometry in the low-dimensional latent semantic indexing (LSI) space, which can be visualized as a conceptual terrain. Then the pages are ranked based on the expected cost to get over the terrain. Results indicate that our terrain based method outperforms traditional readability measures.

As a well-known semantic repository, WordNet is widely used in many applications. However, due to costly edit and maintenance, WordNet's capability of keeping up with the emergence of new concepts is poor compared with on-line encyclopedias such as Wikipedia. To keep WordNet current with folk wisdom, we propose a method to enhance WordNet automatically by merging Wikipedia entities into WordNet, and construct an enriched ontology, named as WorkiNet. WorkiNet keeps the desirable structure of WordNet. At the same time, it captures abundant information from Wikipedia. We also propose a learning approach which is able to generate a tailor-made semantic concept collection for a given document collection. The learning process takes the characteristics of the given document collection into consideration and the semantic concepts in the tailor-made collection can be used as new features for document representation. The experimental results show that the adaptively generated feature space can outperform a static one significantly in text mining tasks, and WorkiNet dominates WordNet most of the time due to its high coverage.

We have developed a framework aiming at normalizing product attributes from Web pages collected from different Web sites without the need of labeled training examples. It can deal with pages composed of different layout format and content in an unsupervised manner. As a result, it can handle a variety of different domains with minimal effort. Our model is based on a generative probabilistic graphical model incorporated with Hidden Markov Models (HMM) considering both attribute names and attribute values to extract and normalize text fragments from Web pages in a unified manner. Dirichlet Process is employed to handle the unlimited number of attributes in a domain. An unsupervised inference method is proposed to predict the unobservable variables. We have also developed a method to automatically construct a domain ontology using the normalized product attributes which are the output of the inference on the graphical model. We have conducted extensive experiments and compared with existing works using prouct Web pages collected from real-world Web sites in three different domains to demonstrate the effectiveness of our framework.

It is well known that synonymous and polysemous terms often bring in some noises when calculating the similarity between documents. Existing ontology-based document representation methods are static, hence, the chosen semantic concept set for representing a document has a fixed resolution and it is not adaptable to the characteristics of a document collection and the text mining problem in hand. We propose an Adaptive Concept Resolution (ACR) model to overcome this issue. ACR can learn a concept border from an ontology taking into consideration of the characteristics of a particular document collection. Then this border can provide a tailor-made semantic concept representation for a document coming from the same domain. Another advantage of ACR is that it is applicable in both classification task where the groups are given in the training document set, and clustering task where no group information is available. Furthermore, the result of this model is not sensitive to the model parameter. The experimental results show that ACR outperforms an existing static method significantly.

Cascaded approach has been used for a long time to conduct sub-tasks in order to accomplish a major task. We put cascaded approach in a probabilistic framework and analyze possible reasons for cascaded errors. To reduce the occurrence of cascaded errors, we need to add a constraint when performing joint training. We suggest a pseudo Conditional Random Field (pseudo-CRF) approach that models two sub-tasks as two Conditional Random Fields (CRFs). We then present the formulation in the context of a linear chain CRF for solving problems on sequence data. In conducting joint training for a pseudo-CRF, we reuse all existing well-developed efficient inference algorithms for a linear chain CRF, which would otherwise require the use of approximate inference algorithms or simulations that involve long computational time. Our experimental results show an interesting fact that a jointly trained CRF model in a pseudo-CRF may perform worse than a separately trained CRF on a sub-task. However the overall system performance of a pseudo-CRF would outperform that of a cascaded approach. We implement the implicit constraint in the form of a soft constraint such that users can define the penalty cost for violating the constraint. In order to work on large-scale datasets, we further suggest a parallel implementation of the pseudo-CRF approach, which can be implemented on a multi-core CPU or GPU on a graphics card that supports multi-threading. Our experimental results show that it can achieve a 12 times increase in speedup.

Dataset shift from the training data in a source domain to the data in a target domain poses a great challenge for many statistical learning methods. Most algorithms can be viewed as exploiting only the first-order statistics, namely, the empirical mean discrepancy to evaluate the distribution gap. Intuitively, considering only the empirical mean may not be statistically efficient. In this paper, we propose a non-parametric distance metric with a good property which jointly considers the empirical mean (Location) and sample covariance (Scatter) difference. More specifically, we propose an improved symmetric Stein's loss function which combines the mean and covariance discrepancy into a unified Bregman matrix divergence of which Jensen-Shannon divergence between normal distributions is a particular case. Our target is to find a good feature representation which can reduce the distribution gap between different domains, at the same time, ensure that the new derived representation can encode most discriminative components with respect to the label information. We have conducted extensive experiments on several document classification datasets to demonstrate the effectiveness of our proposed method.

This paper presents a Bayesian learning framework for adapting information extraction wrappers with new attribute discovery, reducing human effort in extracting precise information from unseen Web sites. Our approach aims at automatically adapting the information extraction knowledge previously learned from a source Web site to a new unseen site, at the same time, discovering previously unseen attributes. Two kinds of text-related clues from the source Web site are considered. The first kind of clue is obtained from the extraction pattern contained in the previously learned wrapper. The second kind of clue is derived from the previously extracted or collected items. A generative model for the generation of the site-independent content information and the site-dependent layout format of the text fragments related to attribute values contained in a Web page is designed to harness the uncertainty involved. Bayesian learning and expectation-maximization (EM) techniques are developed under the proposed generative model for identifying new training data for learning the new wrapper for new unseen sites. Previously unseen attributes together with their semantic labels can also be discovered via another EM-based Bayesian learning based on the generative model. We have conducted extensive experiments from more than 30 real-world Web sites in three different domains to demonstrate the effectiveness of our framework.

Probabilistic graphical models for sequence data enable us to effectively deal with inherent uncertainty in many real-world domains. However, they operate on a mostly propositional level. Logic approaches, on the other hand, can compactly represent a wide variety of knowledge, especially first-order ones, but treat uncertainty only in limited ways. Therefore, combining probability and first-order logic is highly desirable for information extraction which requires uncertainty modeling as well as dependency and deeper knowledge representation. In this paper, we model both segmentations in observation sequence and relations of segments simultaneously in our proposed integrated discriminative probabilistic framework. We propose the Metropolis-Hastings, a Markov chain Monte Carlo (MCMC) algorithm for approximate Bayesian inference to find the maximum a posteriori assignment of all the variables of this model. This integrated model has several advantages over previous probabilistic graphical models, and it offers a great capability of extracting implicit relations and new relation discovery for relation extraction from encyclopedic documents, and capturing sub-structures in named entities for named entity recognition. We performed extensive experiments on the above two well-established information extraction tasks, illustrating the feasibility and promise of our approach.

One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributions. This poses a great difficulty for many statistical learning methods. However, when the distribution in the source domain and the target domain are not identical but related, there may exist a shared concept space to preserve the relation. Consequently a good feature representation can encode this concept space and minimize the distribution gap. To formalize this intuition, we propose a domain adaptation method that parameterizes this concept space by linear transformation under which we explicitly minimize the distribution difference between the source domain with sufficient labeled data and target domains with only unlabeled data, while at the same time minimizing the empirical loss on the labeled data in the source domain. Another characteristic of our method is its capability for considering multiple classes and their interactions simultaneously. We have conducted extensive experiments on two common text mining problems, namely, information extraction and document classification to demonstrate the effectiveness of our proposed method. Copyright 2009 ACM.

We propose a semi-supervised framework incorporating feature mapping with multiclass classification. By learning multiple classification tasks simultaneously, this framework can learn the latent feature space effectively for both labeled and unlabeled data. The knowledge in the transformed space can be transferred not only between the labeled and unlabeled data, but also across multiple classes, so as to improve the classification performance given a small amount of labeled data. We show that this problem is equivalent to a sequential convex optimization problem by applying constraint concave-convex procedure (CCCP). Efficient algorithm with theoretical guarantee is proposed and computational issue is investigated. Extensive experiments have been conducted to demonstrate the effectiveness of our proposed framework.

We have developed an approach for analyzing online job advertisements in different domains (industries) from different regions worldwide. Our approach is able to extract precise information from the text content supporting useful employment market analysis locally and globally. A major component in our approach is an information extraction framework which is composed of two challenging tasks. The first task is to detect unformatted text blocks automatically based on an unsupervised learning model. Identifying these useful text blocks through this learning model allows the generation of highly effective features for the next task which is text fragment extraction learning. The task of text fragment extraction learning is formulated as a domain adaptation model for text fragment classification. One advantage of our approach is that it can easily adapt to a large number of online job advertisements in different and new domains. Extensive experiments have been conducted to demonstrate the effectiveness and flexibility of our approach.

Coreference resolution is regarded as a crucial step for acquiring linkages among pieces of information extracted. Traditionally, coreference resolution models make use of independent attribute-value features over pairs of noun phrases. However, dependency and deeper relations between features can more adequately describe the properties of coreference relations between noun phrases. In this paper, we propose a framework of coreference resolution based on first-order logic and probabilistic graphical model, the Markov Logic Network. The proposed framework enables the use of background knowledge and captures more complex coreference linkage properties through rich expression of conditions. Moreover, the proposed conditions can capture the structural pattern within a noun phrase as well as contextual information between noun phrases. Our experiments show improvement with the use of the expressive logic models and the use of pattern-based conditions.

We have developed an unsupervised framework for simultaneously extracting and normalizing attributes of products from multiple Web pages originated from different sites. Our framework is designed based on a probabilistic graphical model that can model the page-independent content information and the page-dependent layout information of the text fragments in Web pages. One characteristic of our framework is that previously unseen attributes can be discovered from the clue contained in the layout format of the text fragments. Our framework tackles both extraction and normalization tasks by jointly considering the relationship between the content and layout information. Dirichlet process prior is employed leading to another advantage that the number of discovered product attributes is unlimited. An unsupervised inference algorithm based on variational method is presented. The semantics of the normalized attributes can be visualized by examining the term weights in the model. Our framework can be applied to a wide range of Web mining applications such as product matching and retrieval. We have conducted extensive experiments from four different domains consisting of over 300 Web pages from over 150 different Web sites, demonstrating the robustness and effectiveness of our framework.

We propose a cascaded approach for extracting biomedical named entities from text documents using a unified model. Previous works often ignore the high computational cost incurred by a single-phase approach. We alleviate this problem by dividing the named entity extraction task into a segmentation task and a classification task, reducing the computational cost by an order of magnitude. A unified model, which we term "maximum-entropy margin-based" (MEMB), is used in both tasks. The MEMB model considers the error between a correct and an incorrect output during training and helps improve the performance of extracting sparse entity types that occur in biomedical literature. We report experimental evaluations on the GENIA corpus available from the BioNLP/NLPBA (2004) shared task, which demonstrate the state-of-the-art performance achieved by the proposed approach.

This paper investigates a framework that discovers pair-wise constraints for semi-supervised text document clustering. An active learning approach is proposed to select informative document pairs for obtaining user feedbacks. A gain directed document pair selection method that measures how much we can learn by revealing the relationships between pairs of documents is designed. Three different models, namely, uncertainty model, generation error model, and objective function model are proposed. Language modeling is investigated for representing clusters in the semi-supervised document clustering approach.

This paper investigates active learning of constraints for semi-supervised document clustering. We make use of the intermediate clustering results to guide the document pair selection for obtaining user judgments for constraint generation. A gain function is designed for choosing the most informative document pairs given the current cluster assignments. This gain function measures how much we can learn by revealing the judgment of the document pairs. Two methods are investigated, namely, independent gain model and dependent gain model. In the independent gain model, we assume that the information learned by revealing the judgment of a document pair is independent of revealing the judgment of other document pairs. The dependent gain model also considers previously chosen documents to avoid redundant selection and maximize the gain collectively for a set of document pairs. Constrained semi-supervised clustering and gain directed document pair selection are conducted in an iterative manner. We have conducted extensive experiments on several real-world corpora. The results demonstrate that the intermediate clustering assignments and the interactions among a set of document pairs are useful for improving the clustering performance. Our approach is also superior to a recent existing work for this problem.

Abstract The amount of information on the Web has been expanding at an enormous pace. There are a variety of Web documents in different genres, such as news, reports, reviews. Traditionally, the information displayed on Web sites has been static. Recently, there are many Web sites offering content that is dynamically generated and frequently updated. It is also common for Web sites to contain information in different languages since many countries adopt more than one language. Moreover, content may exist in multimedia formats including text, images, video, and audio.

This article introduces a named entity matching model that makes use of both semantic and phonetic evidence. The matching of semantic and phonetic information is captured by a unified framework via a bipartite graph model. By considering various technical challenges of the problem, including order insensitivity and partial matching, this approach is less rigid than existing approaches and highly robust. One major component is a phonetic matching model which exploits similarity at the phoneme level. Two learning algorithms for learning the similarity information of basic phonemic matching units based on training examples are investigated. By applying the proposed named entity matching model, a mining system is developed for discovering new named entity translations from daily Web news. The system is able to discover new name translations that cannot be found in the existing bilingual dictionary.

We develop an unsupervised framework which can collaboratively extract information from multiple Web pages, as well as conduct feature mining tasks in a unified model. Our model allows tight interactions of the two tasks removing the unnecessary boundary between the two tasks. It is beneficial for both tasks since the decisions for information extraction and feature mining can be done in a coherent manner assigning solutions optimizing the quality of both tasks and at the same time eliminating the potential conflicts. Our approach is designed based on an undirected graphical model which can model the inter-dependence between the neighbouring tokens within the same Web page, as well as tokens in different Web pages. Multiple Web pages are considered under this model and the information can be extracted collectively. This design also leads to another characteristic of our framework in that it can conduct mining across Web pages simultaneously. We demonstrate the efficacy of our model by applying it to the important product feature mining application. Extensive experiments on real-world data have been conducted to evaluate our framework.

No abstract available.

Online auction Web sites are fast changing, highly dynamic, and complex as they involve tremendous sellers and potential buyers, as well as a huge amount of items listed for bidding. We develop a two-phase framework which aims at mining and summarizing hot items from multiple auction Web sites to assist decision making. The objective of the first phase is to automatically extract the product features and product feature values of the items from the descriptions provided by the sellers. We design a HMM-based learning method to train an extended HMM model which can adapt to the unseen Web page from which the information is extracted. The goal of the second phase is to discover and summarize the hot items based on the extracted information. We formulate the hot item mining task as a semi-supervised learning problem and employ the graph mincuts algorithm to accomplish this task. The summary of the hot items is then generated by considering the frequency and the position of the product features being mentioned in the descriptions. We have conducted extensive experiments from several real-world auction Web sites to demonstrate the effectiveness of our framework.

We propose a novel lazy learning method called QPAL. QPAL does not simply utilize a kind of distance measure between the query instance and training instances as many lazy learning methods do. It attempts to discover useful patterns known as query projections, which are customized to the query instance. The discovery for useful QPs is conducted in an innovative way. QPAL can guarantee to discover high-quality QPs in the learning process. We use some benchmark data sets and a spam email filtering problem to evaluate QPAL and demonstrate that QPAL achieves good performance and high reliability.

We develop a probabilistic framework which can refine an existing ontology from a source Web site to new unseen sites. One characteristic of our framework is to consider several clues related to how an ontology influences the text content and the visual layout of the Web pages. The first clue is the text fragments regarding the content of the concepts previously collected or extracted from the source Web site. The second clue is the text fragments regarding the header labels of the concepts. The third clue is the visual layout of the text fragments regarding the content of the concepts and the header labels of the concepts in the unseen site. To harness the uncertainty involved in a rigorous manner, we formalize these clues by a generative model to represent the generation of text fragments regarding the concepts and the ontology corresponding to the Web page. Bayesian learning technique and expectation-maximization (EM) algorithm are employed to accomplish the task. Extensive experiments on several real-world Web sites from two different domains have been conducted to demonstrate the effectiveness of our framework.

SEKE is a semantic expectation-based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 327–358, 2005.

Abstract We develop a context‐based generic cross‐lingual retrieval model that can deal with different language pairs. Our model considers contexts in the query translation process. Contexts in the query as well as in the documents based on co‐occurrence statistics from different granularity of passages are exploited. We also investigate cross‐lingual retrieval of automatic generic summaries. We have implemented our model for two different cross‐lingual settings, namely, retrieving Chinese documents from English queries as well as retrieving English documents from Chinese queries. Extensive experiments have been conducted on a large‐scale parallel corpus enabling studies on retrieval performance for two different cross‐lingual settings of full‐length documents as well as automated summaries.

We propose a query-driven lazy learning algorithm which attempts to discover useful local patterns, called support patterns, for classifying a given query. The learning is customized to the query to avoid the horizon effect. We show that this query-driven learning algorithm can guarantee to discover all support patterns with perfect expected accuracy in polynomial time. The experimental results on benchmark data sets also demonstrate that our learning algorithm really has prominent learning performance.

We develop a probabilistic framework for adapting information extraction wrappers with new attribute discovery. Wrapper adaptation aims at automatically adapting a previously learned wrapper from the source Web site to a new unseen site for information extraction. One unique characteristic of our framework is that it can discover new or previously unseen attributes as well as headers from the new site. It is based on a generative model for the generation of text fragments related to attribute items and formatting data in a Web page. To solve the wrapper adaptation problem, we consider two kinds of information from the source Web site. The first kind of information is the extraction knowledge contained in the previously learned wrapper from the source Web site. The second kind of information is the previously extracted or collected items. We employ a Bayesian learning approach to automatically select a set of training examples for adapting a wrapper for the new unseen site. To solve the new attribute discovery problem, we develop a model which analyzes the surrounding text fragments of the attributes in the new unseen site. A Bayesian learning method is developed to discover the new attributes and their headers. EM technique is employed in both Bayesian learning models. We conducted extensive experiments from a number of real-world Web sites to demonstrate the effectiveness of our framework.

SMILE (Scanning Memory Image LatticE) is a lazy learning framework based on a memory image lattice scanning technique. To classify an unseen instance, the instances in the training set will generate a memory image lattice in terms of the similarities between the training instances and the unseen instance. An exploration algorithm of memory image lattice is designed to search an appropriate set of images of training instances to produce the final prediction. SMILE differs from other lazy learning algorithms in that it utilizes subsets of attribute values as much as possible. This design leads to a more flexible model which is less sensitive to data sparseness.

We propose a framework which can exploit hierarchical structures of feature domain values to improve classification performance. Mean-variance analysis method under this framework is investigated. One characteristic of our framework is that it provides a principled way to transform an original feature domain value to a coarser granularity by utilizing the underlying hierarchical structure. Through this transformation, a tradeoff between precision and robustness is achieved to improve the parameter estimation in classification learning. We have conducted an experiment using a biological data set and. The results demonstrate that utilizing domain value hierarchies gains benefits for classification.

We develop a framework which can adapt previously learned information extraction knowledge from the source Web site to new unseen sites. Our framework also makes use of data items previously extracted or collected. Site invariant features are derived from the previously learned extraction knowledge and previously collected items. Multiple text mining methods are employed to automatically discover machine labeled training examples for the new site. Both site invariant and site dependent features of these machine labeled training examples are used to learn the new extraction knowledge. Extensive experiments on real-world Web sites have been conducted to demonstrate the effectiveness of our framework.

We propose a novel named entity matching model which considers both semantic and phonetic clues. The matching is formulated as an optimization problem. One major component is a phonetic matching model which exploits similarity at the phoneme level. We investigate three learning algorithms for obtaining the similarity information of basic phoneme units based on training examples. By applying this proposed named entity matching model, we also develop a mining framework for discovering new, unseen named entity translations from online daily Web news. This framework harvests comparable news in different languages using an existing bilingual dictionary. It is able to discover new name translations not found in the dictionary.

We propose a wrapper adaptation framework which aims at adapting a learned wrapper to an unseen Web site. It significantly reduces human effort in constructing wrappers. Our framework makes use of extraction rules previously discovered from a particular site to seek potential training example candidates for an unseen site. Rule generalization and text categorization are employed for finding suitable example candidates. Another feature of our approach is that it makes use of the previously discovered lexicon to classify good training examples automatically for the new site. We conducted extensive experiments to evaluate the quality of the extraction performance and the adaptability of our approach.

We present a new distributed algorithm for computing the minimum description length (MDL) in learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric and a distributed, asynchronous, adaptive search technique called nagging. Nagging is intrinsically fault-tolerant, has dynamic load balancing features, and scales well. We demonstrate the viability, effectiveness, and scalability of our approach empirically with several experiments using networked machines. More specifically, we show that our distributed algorithm can provide optimal solutions for larger problems as well as good solutions for Bayesian networks of up to 150 variables.

We develop a similarity-based textual document categorization method called the generalized instance set (GIS) algorithm. GIS integrates the advantages of linear classifiers and k-nearest neighbour algorithm by generalization of selected instances. To further enhance the performance, we propose a meta-model framework which combines the strength of different variants of GIS algorithm as well as state-of-the-art existing algorithms using multivariate regression analysis on document feature characteristics. Document feature characteristics, derived from the training document set, capture some inherent properties of a particular category. Different from existing categorization methods, our proposed meta-model can automatically recommend a suitable algorithm for each category based on the category-specific statistical characteristics. In addition, our meta-model differs from existing multi-strategy learning in that our approach is not limited to the number and type of component classifiers. By flexible addition and substitution of different classifiers, incremental classification performance can be obtained. Extensive experiments have been conducted. The results confirm that our meta-model approach can exploit the advantage of its component algorithms, and demonstrate a better performance than existing algorithms.

We investigate a meta-model approach, called Meta-learning Using Document Feature characteristics (MUDOF), for the task of automatic textual document categorization. It employs a meta-learning phase using document feature characteristics. Document feature characteristics, derived from the training document set, capture some inherent category-specific properties of a particular category. Different from existing categorization methods, MUDOF can automatically recommend a suitable algorithm for each category based on the category-specific statistical characteristics. Hence, different algorithms may be employed for different categories. Experiments have been conducted on a real-world document collection demonstrating the effectiveness of our approach. The results confirm that our meta-model approach can exploit the advantage of its component algorithms, and demonstrate a better performance than existing algorithms.

We investigate the modeling of changes in user interest in information filtering systems. A new technique for tracking user interest shifts based on a Bayesian approach is developed. The interest tracker is integrated into a profile learning module of a filtering system. We present an analytical study to establish the rate of convergence for the profile learning with and without the user interest tracking component. We examine the relationship among degree of shift, cost of detection error, and time needed for detection. To study the effect of different patterns of interest shift on system performance we also conducted several filtering experiments. Generally, the findings show that the Bayesian approach is a feasible and effective technique for modeling user interest shift.

Abstract In this article we investigate the use of signature files in Chinese information retrieval system and propose a new partitioning method for Chinese signature file based on the characteristic of Chinese words. Our partitioning method, called Partitioned Signature File for Chinese (PSFC), offers faster search efficiency than the traditional single signature file approach. We devise a general scheme for controlling the trade‐off between the false drop and storage overhead while maintaining the search space reduction in PSFC. An analytical study is presented to support the claims of our method. We also propose two new hashing methods for Chinese signature files so that the signature file will be more suitable for dynamic environment while the retrieval performance is maintained. Furthermore, we have implemented PSFC and the new hashing methods, and we evaluated them using a large‐scale real‐world Chinese document corpus, namely, the TREC‐5 (Text REtrieval Conference) Chinese collection. The experimental results confirm the features of PSFC and demonstrate its superiority over the traditional single signature file method.

Article Free Access Share on Learning to extract hierarchical information from semi-structured documents Authors: Wai-Yip Lin Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile , Wai Lam Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 250–257https://doi.org/10.1145/354756.354826Published:06 November 2000Publication History 13citation848DownloadsMetricsTotal Citations13Total Downloads848Last 12 Months30Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

No abstract available.

We develop an intelligent document delivery approach for filtering text information. Our approach can conduct content-based filtering via a machine learning technique which automatically constructs a filtering profile from training examples. The profiles, encoded in rule representation, are easily understood by human. Good features of high predictive power for the learning process are automatically extracted from the document content. As a result, our approach is able to operate without any prior information or restriction of the topic areas and yet achieve the filtering task. We have conducted an extensive simulation study to analyze the performance of our approach. We have also implemented a practical intelligent news article delivery system based on our approach. Both simulation study as well as practical experiments use real-world document collections and the results demonstrate that our approach is effective. ©1999 John Wiley & Sons, Inc.

We develop an automatic text categorization approach and investigate its application to text retrieval. The categorization approach is derived from a combination of a learning paradigm known as instance-based learning and an advanced document retrieval technique known as retrieval feedback. We demonstrate the effectiveness of our categorization approach using two real-world document collections from the MEDLINE database. Next, we investigate the application of automatic categorization to text retrieval. Our experiments clearly indicate that automatic categorization improves the retrieval performance compared with no categorization. We also demonstrate that the retrieval performance using automatic categorization achieves the same retrieval quality as the performance using manual categorization. Furthermore, detailed analysis of the retrieval performance on each individual test query is provided.

Article Free Access Share on A new on-line learning algorithm for adaptive text filtering Authors: Kwok Leung Yu Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile , Wai Lam Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 156–160https://doi.org/10.1145/288627.288652Online:01 November 1998Publication History 5citation241DownloadsMetricsTotal Citations5Total Downloads241Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Article Free Access Share on Using a generalized instance set for automatic text categorization Authors: Wai Lam Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile , Chao Yang Ho Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile Authors Info & Claims SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrievalAugust 1998 Pages 81–89https://doi.org/10.1145/290941.290961Published:01 August 1998Publication History 121citation930DownloadsMetricsTotal Citations121Total Downloads930Last 12 Months22Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently. In this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A filtering system, SIFTER, has been implemented based on the model, using established techniques in information retrieval and artificial intelligence. These techniques include document representation by a vector-space model, document classification by unsupervised learning, and user modeling by reinforcement learning. The system can filter information based on content and a user's specific interests. The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents. We also describe experimental studies conducted with SIFTER to filter computer and information science documents collected from the Internet and commercial database services. The experimental results demonstrate that the system performs very well in filtering documents in a realistic problem setting.

Article Free Access Share on Detection of shifts in user interests for personalized information filtering Authors: W. Lam Department of Management Sciences, S306 Pappajohn Building, The University of Iowa, Iowa City, Iowa Department of Management Sciences, S306 Pappajohn Building, The University of Iowa, Iowa City, IowaView Profile , S. Mukhopadhyay Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, IN Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, INView Profile , J. Mostafa School of Library and Information Science, Indiana University, Bloomington, IN School of Library and Information Science, Indiana University, Bloomington, INView Profile , M. Palakal Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, IN Computer and Information Science, Purdue University School of Science at Indianapolis, 723 W. Michigan St. SL280, Indianapolis, INView Profile Authors Info & Claims SIGIR '96: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrievalAugust 1996 Pages 317–325https://doi.org/10.1145/243199.243279Published:18 August 1996Publication History 36citation915DownloadsMetricsTotal Citations36Total Downloads915Last 12 Months40Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF