This research studies how Small Data impacts online cyberbullying prediction accuracy. The goal is to understand how different machine learning methods, including deep learning, behave when the training data is very small (i.e. Small Data). We design training sets with different sizes, vary learning tasks from binary to multiple classes, and study the algorithm’s performance. The study draws conclusions on when and how machine learning methods should be considered for cyberbullying predictions using small data sets.

In this paper, we propose a new deep learning based method for suggestion mining. The major challenges of suggesion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multimodel Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.

Training a neural network model can be a lifelong learning process and is a computationally intensive one. A severe adverse effect that may occur in deep neural network models is that they can suffer from catastrophic forgetting during retraining on new data. To avoid such disruptions in the continuous learning, one appealing property is the additive nature of ensemble models. In this paper, we propose two generic ensemble approaches, gradient boosting and meta-learning, to solve the catastrophic forgetting problem in tuning pre-trained neural network models.

on Web Information Systems Engineering (WISE 2015) was held in Miami, Florida, USA, November 1-3, 2015.Building on the success of its predecessors, WISE 2015 continues to be a major international forum for researchers, professionals, and industrial practitioners to share their knowledge in the rapidly growing area of Web technologies, methodologies, and applications.The first WISE event took place in Hong Kong, China (2000).Then the trip continued to Kyoto, Japan (2001); Singapore (2002);

In disaster management, people are interested in the development and the evolution of the disasters. If they intend to track the information of the disaster, they will be overwhelmed by the large number of disaster-related documents, microblogs, and news, etc. To support disaster management and minimize the loss during the disaster, it is necessary to efficiently and effectively collect, deliver, summarize, and analyze the disaster information, letting people in affected area quickly gain an overview of the disaster situation and improve their situational awareness.

Update summarization aims to generate brief summaries of recent documents to capture new information different from earlier documents. In this paper, we propose a new method to generate the sentence similarity graph using a novel similarity measure based on Helliger distance and apply semi-supervised learning on the sentence graph to select the sentences with maximum consistency and minimum redundancy to form the summaries. We use TAC 2011 data to evaluate our proposed method and compare it with existing baselines. The experimental results show the effectiveness of our proposed method.

Popular online publishers produce huge amount of news articles every day, so it is important to summarize the most up-to-the-minute information to help users quickly know the progresses of their interested news events. In this paper, we develop NewsCubeSum, a novel personalized news summarization system utilizing OLAP and supervised sentence selection techniques to generate brief summaries delivering news updates in multiple dimensions (such as time, entity, and topic). An illustrative case study and experimental results on summarization performance comparisons are provided to show the effectiveness of NewsCubeSum.

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grasp the linkage between different technologies in order to better understand the technical trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing the relationships between technologies. In this paper, we propose a unified framework, named PatentDom, to identify important patents related to key techniques from a large number of patent documents. The framework integrates different types of patent information, including patent content, citations of patents, and temporal relations, and provides a concise yet comprehensive technology summary. The identified key patents enable a variety of patent-related analytical applications, e.g., outlining the technology evolution of a particular domain, tracing a given technique to prior technologies, and mining the technical connection of two given patent documents. Empirical analysis and extensive case studies on a collection of US patent documents demonstrate the efficacy of our proposed framework.

Given a short query audio clip, the goal of audio retrieval is to automatically fetch all similar clips from a given audio database. Different from traditional audio similarity which is mainly based on priori knowledge of objective reality, this paper proposes to use a more subjective method to m

Given a collection of document groups, a natural question is to identify the differences among these groups. Although traditional document summarization techniques can summarize the content of the document groups one by one, there exists a great necessity to generate a summary of the differences among the document groups. In this article, we study a novel problem of summarizing the differences between document groups. A discriminative sentence selection method is proposed to extract the most discriminative sentences that represent the specific characteristics of each document group. Experiments and case studies on real-world data sets demonstrate the effectiveness of our proposed method.

This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.

Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on real-time events. In this paper we explore the problem of generating storylines from microblogs for user input queries. This problem is challenging due to the sparse, dynamic and social nature of microblogs. Given a query of an ongoing event, we propose to sketch the real-time storyline of the event by a two-level solution. We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization. Comprehensive experiments on Twitter data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

Identifying functional modules from protein-protein interaction networks is an important and challenging task. This paper presents a new approach called PPIBM which is designed to integrate gene expression data analysis and clustering of protein-protein interactions. The proposed approach relies on a Bayesian model which uses as its base protein-protein interactions given as part of input. The proposed method is evaluated with standard measures and its performance is compared with the state-of-the-art network analysis methods. Experimental results on both real-world data and synthetic data demonstrate the effectiveness of the proposed approach.

With the rapid growth of social media websites, microblogging has become a popular way to spread instant news and events. Due to the dynamic and social nature of microblogs, extracting useful information from microblogs is more challenging than from the traditional news articles. In this paper we study the problem of summarizing the differences from microblogs. Given a collection of microblogs discussing an event/topic, we propose to generate a short summary delivering the differences among these microblogs, such as the different points of view for a news topic and the changes and evolution of an ongoing event.

Given a collection of document groups, a natural question is to identify the differences among them. Although traditional document summarization techniques can summarize the content of the document groups one by one, there exists a great necessity to generate a summary of the differences among the document groups. In this article, we study a novel problem, that of summarizing the differences between document groups. A discriminative sentence selection method is proposed to extract the most discriminative sentences which represent the specific characteristics of each document group. Experiments and case studies on real-world data sets demonstrate the effectiveness of our proposed method.

Recommending news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. However, the latent relationships among different news items, and the special properties of new articles, such as short shelf lives and value of immediacy, render the previous approaches inefficient.

Document understanding techniques such as document clustering and multidocument summarization have been receiving much attention recently. Current document clustering methods usually represent the given collection of documents as a document-term matrix and then conduct the clustering process. Although many of these clustering methods can group the documents effectively, it is still hard for people to capture the meaning of the documents since there is no satisfactory interpretation for each document cluster. A straightforward solution is to first cluster the documents and then summarize each document cluster using summarization methods. However, most of the current summarization methods are solely based on the sentence-term matrix and ignore the context dependence of the sentences. As a result, the generated summaries lack guidance from the document clusters. In this article, we propose a new language model to simultaneously cluster and summarize documents by making use of both the document-term and sentence-term matrices. By utilizing the mutual influence of document clustering and summarization, our method makes; (1) a better document clustering method with more meaningful interpretation; and (2) an effective document summarization method with guidance from document clustering. Experimental results on various document datasets show the effectiveness of our proposed method and the high interpretability of the generated summaries.

Due to the importance of high-quality customer service, many companies use intelligent helpdesk systems (e.g., case-based systems) to improve customer service quality. However, these systems face two challenges: 1) Case retrieval measures: most case-based systems use traditional keyword-matching-based ranking schemes for case retrieval and have difficulty to capture the semantic meanings of cases and 2) result representation: most case-based systems return a list of past cases ranked by their relevance to a new request, and customers have to go through the list and examine the cases one by one to identify their desired cases. To address these challenges, we develop iHelp, an intelligent online helpdesk system, to automatically find problem-solution patterns from the past customer-representative interactions. When a new customer request arrives, iHelp searches and ranks the past cases based on their semantic relevance to the request, groups the relevant cases into different clusters using a mixture language model and symmetric matrix factorization, and summarizes each case cluster to generate recommended solutions. Case and user studies have been conducted to show the full functionality and the effectiveness of iHelp.

Query-based multi-document summarization aims to create a short summary given a collection of documents and a query. Most of the existing methods treat the query as one single sentence and rank the sentences in the documents based on their similarities with the query sentence. However, these methods lack of intensive analysis on the given query which typically consist of several topic aspects. In this paper, we propose a topic aspect extraction method to discover the aspect words and sentences contained in the query narrative texts and the input documents, and then incorporate these aspect words and sentences into a cross propagation model based on the sentence-term bipartite graph for document summarization. Experiments on DUC benchmark data show the effectiveness of our proposed approach on the topic-driven document summarization task.

Document summarization has become a hot topic in recent years. However, most of existing summarization methods work on a batch of documents and do not consider that documents may arrive in a sequence and the corresponding summaries need to be updated in real time. In this paper, we propose a new summarization method based on an incremental hierarchical clustering framework to update summaries as soon as a new document arrives. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.

Keyword (Feature) selection enhances and improves many Information Retrieval (IR) tasks such as document categorization, automatic topic discovery, etc. The problem of keyword selection is usually solved using supervised algorithms. In this paper, we propose an unsupervised approach that combines keyword selection and document clustering (topic discovery) together. The proposed approach extends non-negative matrix factorization (NMF) by incorporating a weight matrix to indicate the importance of the keywords. The proposed approach is further extended to a weighted version in which each document is also assigned a weight to assess its importance in the cluster. This work considers both theoretical and empirical weighted feature subset selection for NMF and draws the connection between unsupervised feature selection and data clustering. We apply our proposed approaches to various document understanding tasks including document clustering, summarization, and visualization. Experimental results demonstrate the effectiveness of our approach for these tasks.

In this poster, we propose a novel document summarization approach named Ontology-enriched Multi-Document Summarization(OMS) for utilizing background knowledge to improve summarization results. OMS first maps the sentences of input documents onto an ontology, then links the given query to a specific node in the ontology, and finally extracts the summary from the sentences in the subtree rooted at the query node. By using the domain-related ontology, OMS can better capture the semantic relevance between the query and the sentences, and thus lead to better summarization results. As a byproduct, the final summary generated by OMS can be represented as a tree showing the hierarchical relationships of the extracted sentences. Evaluation results on the collection of press releases by Miami-Dade County Department of Emergency Management during Hurricane Wilma in 2005 demonstrate the efficacy of OMS.

In this paper, we propose feature subset non-negative matrix factorization (NMF), which is an unsupervised approach to simultaneously cluster data points and select important features. We apply our proposed approach to various document understanding tasks including document clustering, summarization, and visualization. Experimental results demonstrate the effectiveness of our approach for these tasks.

Given a collection of documents, various multi-document summarization methods have been proposed to generate a short summary. However, few studies have been reported on aggregating different summarization methods to possibly generate better summarization results. We propose a weighted consensus summarization method to combine the results from single summarization systems. Experimental results on DUC2004 data sets demonstrate the performance improvement by aggregating multiple summarization systems, and our proposed weighted consensus summarization method outperforms other combination methods.

Given a collection of documents, most of existing multidocument summarization methods automatically generate a static summary for all the users. However, different users may have different opinions on the documents, thus there is a necessity for improving users' interactions in the summarization process. In this paper, we propose an interactive document summarization system using information visualization techniques.

Gene expression data usually contain a large number of genes but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. Using machine learning techniques, traditional gene selection based on empirical mutual information suffers the data sparseness issue due to the small number of samples. To overcome the sparseness issue, we propose a model-based approach to estimate the entropy of class variables on the model, instead of on the data themselves. Here, we use multivariate normal distributions to fit the data, because multivariate normal distributions have maximum entropy among all real-valued distributions with a specified mean and standard deviation and are widely used to approximate various distributions. Given that the data follow a multivariate normal distribution, since the conditional distribution of class variables given the selected features is a normal distribution, its entropy can be computed with the log-determinant of its covariance matrix. Because of the large number of genes, the computation of all possible log-determinants is not efficient. We propose several algorithms to largely reduce the computational cost. The experiments on seven gene data sets and the comparison with other five approaches show the accuracy of the multivariate Gaussian generative model for feature selection, and the efficiency of our algorithms.

Given a collection of document groups, a quick question is what are the differences in these groups. In this paper, we study a novel problem of summarizing the differences between document groups. A discriminative sentence selection method is proposed to extract the most discriminative sentences which represent the specific characteristics of each document group. Experiments on real world data sets demonstrate the effectiveness of our proposed method.

Label propagation is an approach to assign class labels to unlabeled data given some partially labeled data. In this paper, we systematically generalize the Laplacian matrix based label propagation method from pairwise graph data to data objects described by bipartite and general K-partite graphs. By deriving explicit label propagation formula, we show how information on one type of variables can be transformed to other types of variables. For example, in a word-document-author multi-relational dataset, information on words and on authors can effectively enhance the document labeling. Motivating examples are presented to illustrate these new concepts. Extensive experiments are performed on real-life datasets to show the effectiveness of our label propagation.

In this poster, we develop an evolutionary document summarization system for discovering the changes and differences in each phase of a disaster evolution. Given a collection of document streams describing an event, our system generates a short summary delivering the main development theme of the event by extracting the most representative and discriminative sentences at each phase. Experimental results on the collection of press releases for Hurricane Wilma in 2005 demonstrate the efficacy of our proposal.

Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach.

Document understanding techniques such as document clustering and multi-document summarization have been receiving much attention in recent years. Current document clustering methods usually represent documents as a term-document matrix and perform clustering algorithms on it. Although these clustering methods can group the documents satisfactorily, it is still hard for people to capture the meanings of the documents since there is no satisfactory interpretation for each document cluster. In this paper, we propose a new language model to simultaneously cluster and summarize the documents. By utilizing the mutual influence of the document clustering and summarization, our method makes (1) a better document clustering method with more meaningful interpretation and (2) a better document summarization method taking the document context information into consideration.

Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent to normalized spectral clustering, is used to group sentences into clusters. Finally, the most informative sentences are selected from each group to form the summary. Experimental results on DUC2005 and DUC2006 data sets demonstrate the improvement of our proposed framework over the implemented existing summarization systems. A further study on the factors that benefit the high performance is also conducted.

Helpdesk databases are used to store past interactions between customers and companies to improve customer service quality. One common scenario of using helpdesk database is to find whether recommendations exist given a new problem from a customer. However, customers often provide incomplete or even inaccurate information. Manually preparing a list of clarification questions does not work for large databases. This paper investigates the problem of automatic generation of a minimal number of questions to reach an appropriate recommendation. This paper proposes a novel dynamic active probing method. Compared to other alternatives such as decision tree and case-based reasoning, this method has two distinctive features. First, it actively probe the customer to get useful information to reach the recommendation, and the information provided by customer will be immediately used by the method to dynamically generate the next questions to probe. This feature ensures that all available information from the customer is used. Second, this method is based on a probabilistic model, and uses a data augmentation method which avoids overfitting when estimating the probabilities in the model. This feature ensures that the method is robust to databases that are incomplete or contain errors. Experimental results verify the effectiveness of our approach.

The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic and new, previously unseen malicious executables. In this paper, resting on the analysis of Windows API execution sequences called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of King-Soft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system out perform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques.