Dehong Gao

A unified graph model for personalized query-oriented reference paper recommendation

With the tremendous amount of research publications, it has become increasingly important to provide a researcher with a rapid and accurate recommendation of a list of reference papers about a research field or topic. In this paper, we propose a unified graph model that can easily incorporate various types of useful information (e.g., content, authorship, citation and collaboration networks etc.) for efficient recommendation. The proposed model not only allows to thoroughly explore how these types of information can be better combined, but also makes personalized query-oriented reference paper recommendation possible, which as far as we know is a new issue that has not been explicitly addressed in the past. The experiments have demonstrated the clear advantages of personalized recommendation over non-personalized recommendation.

Cross-lingual sentiment lexicon learning with bilingual word graph label propagation

In this article we address the task of cross-lingual sentiment lexicon learning, which aims to automatically generate sentiment lexicons for the target languages with available English sentiment lexicons. We formalize the task as a learning problem on a bilingual word graph, in which the intra-language relations among the words in the same language and the inter-language relations among the words between different languages are properly represented. With the words in the English sentiment lexicon as seeds, we propose a bilingual word graph label propagation approach to induce sentiment polarities of the unlabeled words in the target language. Particularly, we show that both synonym and antonym word relations can be used to build the intra-language relation, and that the word alignment information derived from bilingual parallel sentences can be effectively leveraged to build the inter-language relation. The evaluation of Chinese sentiment lexicon learning shows that the proposed approach outperforms existing approaches in both precision and recall. Experiments conducted on the NTCIR data set further demonstrate the effectiveness of the learned sentiment lexicon in sentence-level sentiment classification.

Twitter hyperlink recommendation with user-tweet-hyperlink three-way clustering

Twitter, the most famous micro-blogging service and online social network, collects millions of tweets every day. Due to the length limitation, users usually need to explore other ways to enrich the content of their tweets. Some studies have provided findings to suggest that users can benefit from added hyperlinks in tweets. In this paper, we focus on the hyperlinks in Twitter and propose a new application, called hyperlink recommendation in Twitter. We expect that the recommended hyperlinks can be used to enrich the information of user tweets. A three-way tensor is used to model the user-tweet-hyperlink collaborative relations. Two tensor-based clustering approaches, tensor decomposition-based clustering (TDC) and tensor approximation-based clustering (TAC) are developed to group the users, tweets and hyperlinks with similar interests, or similar contexts. Recommendation is then made based on the reconstructed tensor using cluster information. The evaluation results in terms of Mean Absolute Error (MAE) shows the advantages of both the TDC and TAC approaches over a baseline recommendation approach, i.e., memory-based collaborative filtering. Comparatively, the TAC approach achieves better performance than the TDC approach.

Opinion influence and diffusion in social network

Nowadays, more and more people tend to make decisions based on the opinion information from the Internet, in addition to recommendations from offline friends or parents. For example, we may browse the resumes and comments on election candidates to determine if one candidate is qualified, or consult the consumer reports or reviews on special e-commercial websites to decide which brand of computer is suitable for ones needs. Though opinion information is rich on the Internet, [2] points out that 58% of American Internet users deem that online information is irretrievable, confusing, or conflicting with each other. Early works on opinion mining help to classify opinion polarity, to extract specific opinions and to summarize opinion texts. However, all these works are usually based on plain texts (reviews, comments or news articles). With the explosion of Web 2.0 applications, especially social network applications like blogs, discussion forums, micro-blogs, the massive individual users go to the major media websites, which leads to much more opinion materials posted on the Internet by user-shared experiences or views [3]. These opinion-rich and social network-based applications bring new perspectives for opinion mining as well. First, in addition to plain texts (reviews, newswire) in traditional opinion mining, we see new types of cyber-based text, like personal diary blogs, cyber-SMS tweets. Second, if we regard the opinions in plain text as static, the dynamic change of opinions in the social network is a new promising area, and catch increasing attention of worldwide researchers. In the social network, the opinion held by one individual is not static, but changes, which can be influenced by others. A serial of changes among different users forms the opinion propagation or diffusion in the network. This paper and my doctoral work focus on the opinion influence and diffusion in the social network, which explore the detailed process of one-to-one influence and the opinion diffusion process in the social network. The significance of this work is it can benefit many other related researches, like information maximum, viral marketing. Now some pioneering works have been conducted to investigate the role of social networks in information diffusion and influencers in the social network. These works are usually based on information diffusion models, like the cascade model (CM) or epidemic model (EM). However, we argue that it is not enough to simply apply these models to opinion influence and diffusion. 1) For both CM and EM, status shift is along specific directions, from inactive to active (CM) or from susceptible to infectious, and then, to recovered (EM). But opinion influence is more complex.

Learning features through feedback for blog distillation

The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.

Coherent narrative summarization with a cognitive model

Borrowing theories from cognitive psychology, we propose a computational model of human cognition.Using the cognitive model, we generate coherent narrative summaries.We propose a novel method of proposition-level extractive summarization.We verify the cognitive model and summarization method with narrative text data. For summary readers, coherence is no less important than informativeness and is ultimately measured in human terms. Taking a human cognitive perspective, this paper is aimed to generate coherent summaries of narrative text by developing a cognitive model. To model coherence with a cognitive background, we simulate the long-term human memory by building a semantic network from a large corpus like Wiki and design algorithms to account for the information flow among different compartments of human memory. Proposition is the basic processing unit for the model. After processing a whole narrative in a cyclic way, our model supplies information to be used for extractive summarization on the proposition level. Experimental results on two kinds of narrative text, newswire articles and fairy tales, show the superiority of our proposed model to several representative and popular methods.

Towards content-level coherence with aspect-guided summarization

The TAC 2010 summarization track initiated a new task—aspect-guided summarization—that centers on textual aspects embodied as particular kinds of information of a text. We observe that aspect-guided summaries not only address highly specific user need, but also facilitate content-level coherence by using aspect information. In this article, we present a full-fledged approach to aspect-guided summarization with a focus on summary coherence. Our summarization approach depends on two prerequisite subtasks: recognizing aspect-bearing sentences in order to do sentence extraction, and modeling aspect-based coherence with an HMM model in order to predict a coherent sentence ordering. Using the manually annotated TAC 2010 and 2010 datasets, we validated the effectiveness of our proposed methods for those subtasks. Drawing on the empirical results, we proceed to develop an aspect-guided summarizer based on a simple but robust base summarizer. With sentence selection guided by aspect information, our system is one of the best on TAC 2011. With sentence ordering predicted by the aspect-based HMM model, the summaries achieve good coherence.

LDA-Based Topic Formation and Topic-Sentence Reinforcement for Graph-Based Multi-document Summarization

In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.