With the booming development of the Web, popular Chinese forums enable people to find experienced customers' reviews for products. In order to get an all-around opinion about one product, users need to go through plenty of web pages, which is time-consuming and inefficient. Consequently, automatic review mining and summarization has become a hot research topic recently. However, previous approaches are not applicable for mining Chinese customer reviews. In this paper, we introduce SOPING, a Chinese customer review mining system that mines reviews from forums. Specifically, we propose a novel search-based approach to extract product features and a feature-oriented sentence orientation determination method. Our experimental results show that our proposed techniques are highly effective.

Blogs have been expanded at an incredible speed in recent years. Plentiful personal information makes blogs a popular way mining user profiles. In this paper, we propose a novel bloggers' interests modeling approach based on forgetting mechanism. A new forgetting function is introduced to track interest drift. Based on that, the Short Term Interest Models (STIM) and Long Term Interest Models (LTIM) are constructed to describe bloggers' short-term and long-term interests. The experiments show that both models can identify bloggers' preferences well respectively.

This poster focuses on the study of term context dependence in the application of sentence retrieval. Based on Markov Random Field (MRF), three forms of dependence among query terms are considered. Under different assumptions of term dependence relationship, three feature functions are defined, with the purpose to utilize association features between query terms in sentence to evaluate the relevance of sentence. Experimental results have proven the efficiency of the proposed retrieval models in improving the performance of sentence retrieval.

No abstract available.

Query ambiguity prevents existing retrieval systems from returning reasonable results for every query. As there is already lots of work done on resolving ambiguity, vague queries could be handled using corresponding approaches separately if they can be identified in advance. Quantification of the degree of (lack of) ambiguity laysthe groundwork for the identification. In this poster, we propose such a measure using query topics based on the topic structure selected from the Open Directory Project (ODP) taxonomy. We introduce clarity score to quantify the lack of ambiguity with respect to data sets constructed from the TREC collections and the rank correlation test results demonstrate a strong positive association between the clarity scores and retrieval precisions for queries.

This paper makes an intensive investigation of the application of Bayesian network in sentence retrieval and introduces three Bayesian network based sentence retrieval models with or without consideration of term relationships. Term relationships in this paper are considered from two perspectives: relationships between pairs of terms and relationships between terms and term sets. Experiments have proven the efficiency of Bayesian network in the application of sentence retrieval. Particularly, retrieval result with consideration of the second kind of term relationship performs better in improving retrieval precision.