Restaurant dishes represent a significant portion of food that people consume in their daily life. While people are becoming health-conscious in their food intake, convenient restaurant food tracking becomes an essential task in wellness and fitness applications. Given the huge number of dishes (food categories) involved, it becomes extremely challenging for traditional food photo classification to be feasible in both algorithm design and training data availability. In this work, we present a demo that runs on restaurant dish images in a city of millions of residents and tens of thousand restaurants. We propose a rank-loss based convolutional neural network to optimize the image features representation. Context information such as GPS location of the recognition request is also used to further improve the performance. Our experimental results are highly promising. We have shown in our demo that the proposed algorithm is near ready to be deployed in real-world applications.

Document summarization is playing an important role in coping with information overload on the Web. Many summarization models have been proposed recently, but few try to adjust the summary length and sentence order according to application scenarios. With the popularity of handheld devices, presenting key information first in summaries of flexible length is of great convenience in terms of faster reading and decision-making and network consumption reduction. Targeting this problem, we introduce a novel task of generating summaries of incremental length. In particular, we require that the summaries should have the ability to automatically adjust the coverage of general-detailed information when the summary length varies. We propose a novel summarization model that incrementally maximizes topic coverage based on the document’s hierarchical topic model. In addition to the standard Rouge-1 measure, we define a new evaluation metric based on the similarity of the summaries’ topic coverage distribution in order to account for sentence order and summary length. Extensive experiments on Wikipedia pages, DUC 2007, and general noninverted writing style documents from multiple sources show the effectiveness of our proposed approach. Moreover, we carry out a user study on a mobile application scenario to show the usability of the produced summary in terms of improving judgment accuracy and speed, as well as reducing the reading burden and network traffic.

In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the key concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the key and non-key concepts, while the differences between the key concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of key concepts in the queries, but also fail to consider concept level expansion for them. In this paper, we explore a key concept identification approach for query refinement and a pivot language translation based approach to explore key concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the key concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval.

Volunteers have always been extremely crucial and in urgent need for nonprofit organizations (NPOs) to sustain their continuing operations. However, it is expensive and time-consuming to recruit volunteers using traditional approaches. In the Web 2.0 era, abundant and ubiquitous social media data opens a door to the possibility of automatic volunteer identification. In this article, we aim to fully explore this possibility by proposing a scheme that is able to predict users’ volunteerism tendency from user-generated contents collected from multiple social networks based on a conceptual volunteering decision model. We conducted comprehensive experiments to investigate the effectiveness of our proposed scheme and further discussed its generalizibility and extendability. This novel interdisciplinary research will potentially inspire more promising and important human-centered applications.

User generated contents (UGCs) from various social media sites give analysts the opportunity to obtain a comprehensive and dynamic view of any topic from multiple heterogeneous information sources. Summarization provides a promising means of distilling the overview of the targeted topic by aggregating and condensing the related UGCs. However, the mass volume, uneven quality, and dynamics of UGCs, pose new challenges that are not addressed by existing multi-document summarization techniques. In this paper, we introduce a timely task of dynamic structural and textual summarization. We generate topic hierarchy from the UGCs as a high level overview and structural guide for exploring and organizing the content. To capture the evolution of events in the content, we propose a unified dynamic reconstruction approach to detect the update points and generate the time-sequence textual summary. To enhance the expressiveness of the reconstruction space, we further use the topic hierarchy to organize the UGCs and the hierarchical subtopics to augment the sentence representation. Experimental comparison with the state-of-the-art summarization models on a multi-source UGC dataset shows the superiority of our proposed methods. Moreover, we conducted a user study on our usability enhancement measures. It suggests that by disclosing some meta information of the summary generation process in the proposed framework, the time-sequence textual summaries can pair with the structural overview of the topic hierarchy to achieve interpretable and verifiable summarization.

With the popularity of social media platforms such as Facebook and Twitter, the amount of useful data in these sources is rapidly increasing, making them promising places for information acquisition. This research aims at the customized organization of a social media corpus using focused topic hierarchy. It organizes the contents into different structures to meet with users' different information needs (e.g., "iPhone 5 problem" or "iPhone 5 camera"). To this end, we introduce a novel function to measure the likelihood of a topic hierarchy, by which the users' information need can be incorporated into the process of topic hierarchy construction. Using the structure information within the generated topic hierarchy, we then develop a probability based model to identify the representative contents for topics to assist users in document retrieval on the hierarchy. Experimental results on real world data illustrate the effectiveness of our method and its superiority over state-of-the-art methods for both information organization and retrieval tasks.

User generated contents (UGCs) carry a huge amount of high quality information. However, the information overload and diversity of UGC sources limit their potential uses. In this research, we propose a framework to organize information from multiple UGC sources by a topic hierarchy which is automatically generated and updated using the UGCs. We explore the unique characteristics of UGCs like blogs, cQAs, microblogs, etc., and introduce a novel scheme to combine them. We also propose a graph-based method to enable incremental update of the generated topic hierarchy. Using the hierarchy, users can easily obtain a comprehensive, in-depth and up-to-date picture of their topics of interests. The experiment results demonstrate how information from multiple heterogeneous sources improves the resultant topic hierarchies. It also shows that the proposed method achieves better F1 scores in hierarchy generation as compared to the state-of-the-art methods.

Recently, statistical topic modeling has been widely applied in text mining and knowledge management due to its powerful ability. A topic, as a probability distribution over words, is usually difficult to be understood. A common, major challenge in applying such topic models to other knowledge management problem is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, previous works simply treat topics individually without considering the hierarchical relation among topics, and less attention has been paid to creating a good hierarchical topic descriptors for a hierarchy of topics. In this paper, we propose two effective algorithms that automatically assign concise labels to each topic in a hierarchy by exploiting sibling and parent-child relations among topics. The experimental results show that the inter-topic relation is effective in boosting topic labeling accuracy and the proposed algorithms can generate meaningful topic labels that are useful for interpreting the hierarchical topics.

This paper proposes a novel Product Comparison approach. The comparative relations between products are first mined from both user reviews on multiple review websites and community-based question answering pairs containing product comparison information. A unified graph model is then developed to integrate the resultant comparative relations for product comparison. Experiments on popular electronic products show that the proposed approach outperforms the state-of-the-art methods.

Community Question Answering services, e.g., Yahoo! Answers, have accumulated large archives of question answer (QA) pairs for information and answer retrieval. An effective question retrieval model is essential to increase the accessibility of the QA archives. QA archives are usually organized into categories and question search can be performed within the whole collection or within a certain category..

This paper presents a novel prototype hierarchy based clustering (PHC) framework for the organization of web collections. It solves simultaneously the problem of categorizing web collections and interpreting the clustering results for navigation. By utilizing prototype hierarchies and the underlying topic structures of the collections, PHC is modeled as a multi-criterion optimization problem based on minimizing the hierarchy evolution, maximizing category cohesiveness and inter-hierarchy structural and semantic resemblance. The flexible design of metrics enables PHC to be a general framework for applications in various domains. In the experiments on categorizing 4 collections of distinct domains, PHC achieves 30% improvement in ¼F1 over the state-of-the-art techniques. Further experiments provide insights on performance variations with abstract and concrete domains, completeness of the prototype hierarchy, and effects of different combinations of optimization criteria.

Existing question retrieval models work relatively well in finding similar questions in community-based question answering (cQA) services. However, they are designed for single-sentence queries or bag-of-word representations, and are not sufficient to handle multi-sentence questions complemented with various contexts. Segmenting questions into parts that are topically related could assist the retrieval system to not only better understand the user's different information needs but also fetch the most appropriate fragments of questions and answers in cQA archive that are relevant to user's query. In this paper, we propose a graph based approach to segmenting multi-sentence questions. The results from user studies show that our segmentation model outperforms traditional systems in question segmentation by over 30% in user's satisfaction. We incorporate the segmentation model into existing cQA question retrieval framework for more targeted question matching, and the empirical evaluation results demonstrate that the segmentation boosts the question retrieval performance by up to 12.93% in Mean Average Precision and 11.72% in Top One Precision. Our model comes with a comprehensive question detector equipped with both lexical and syntactic features.

While traditional question answering (QA) systems tailored to the TREC QA task work relatively well for simple questions, they do not suffice to answer real world questions. The community-based QA systems offer this service well, as they contain large archives of such questions where manually crafted answers are directly available. However, finding similar questions in the QA archive is not trivial. In this paper, we propose a new retrieval framework based on syntactic tree structure to tackle the similar question matching problem. We build a ground-truth set from Yahoo! Answers, and experimental results show that our method outperforms traditional bag-of-word or tree kernel based methods by 8.3% in mean average precision. It further achieves up to 50% improvement by incorporating semantic features as well as matching of potential answers. Our model does not rely on training, and it is demonstrated to be robust against grammatical errors as well.