Publication statistics

Pub. period:2004-2010
Pub. count:9
Number of co-authors:18


Number of publications with 3 favourite co-authors:

Xin Li:
Gordon Sun:
Zhaohui Zheng:



Productive colleagues

Jing Bai's 3 most productive colleagues in number of publications:

Jian-Yun Nie:33
Hongyuan Zha:32
Xin Li:23

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Jing Bai


Publications by Jing Bai (bibliography)

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Dong, Anlei, Zhang, Ruiqiang, Kolari, Pranam, Bai, Jing, Diaz, Fernando, Chang, Yi, Zheng, Zhaohui and Zha, Hongyuan (2010): Time is of the essence: improving recency ranking using Twitter data. In: Proceedings of the 2010 International Conference on the World Wide Web 2010. pp. 331-340.

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.

© All rights reserved Dong et al. and/or their publisher

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Bai, Jing, Chang, Yi, Cui, Hang, Zheng, Zhaohui, Sun, Gordon and Li, Xin (2008): Investigation of partial query proximity in web search. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1183-1184.

Proximity of query terms in a document is an important criterion in IR. However, no investigation has been made to determine the most useful term sequences for which proximity should be considered. In this study, we test the effectiveness of using proximity of partial term sequences (n-grams) for Web search. We observe that the proximity of sequences of 3 to 5 terms is most effective for long queries, while shorter or longer sequences appear less useful. This suggests that combinations of 3 to 5 terms can best capture the intention in user queries. In addition, we also experiment with weighing the importance of query sub-sequences using query log frequencies. Our preliminary tests show promising empirical results.

© All rights reserved Bai et al. and/or ACM Press

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Bai, Jing, Nie, Jian-Yun, Cao, Guihong and Bouchard, Hugues (2007): Using query contexts in information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2007. pp. 15-22.

User query is an element that specifies an information need, but it is not the only one. Studies in literature have found many contextual factors that strongly influence the interpretation of a query. Recent studies have tried to consider the user's interests by creating a user profile. However, a single profile for a user may not be sufficient for a variety of queries of the user. In this study, we propose to use query-specific contexts instead of user-centric ones, including context around query and context within query. The former specifies the environment of a query such as the domain of interest, while the latter refers to context words within the query, which is particularly useful for the selection of relevant term relations. In this paper, both types of context are integrated in an IR model based on language modeling. Our experiments on several TREC collections show that each of the context factors brings significant improvements in retrieval effectiveness.

© All rights reserved Bai et al. and/or ACM Press

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Cao, Guihong, Gao, Jianfeng, Nie, Jian-Yun and Bai, Jing (2007): Extending query translation to cross-language query expansion with markov chain models. In: Silva, Mario J., Laender, Alberto H. F., Baeza-Yates, Ricardo A., McGuinness, Deborah L., Olstad, Bjrn, Olsen, ystein Haug and Falco, Andr O. (eds.) Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management - CIKM 2007 November 6-10, 2007, Lisbon, Portugal. pp. 351-360.

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Cao, Guihong, Nie, Jian-Yun and Bai, Jing (2006): Constructing better document and query models with markov chains. In: Yu, Philip S., Tsotras, Vassilis J., Fox, Edward A. and Liu, Bing (eds.) Proceedings of the 2006 ACM CIKM International Conference on Information and Knowledge Management November 6-11, 2006, Arlington, Virginia, USA. pp. 800-801.

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Gao, Guihong, Nie, Jian-Yun and Bai, Jing (2005): Integrating word relationships into language models. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2005. pp. 298-305.

In this paper, we propose a novel dependency language modeling approach for information retrieval. The approach extends the existing language modeling approach by relaxing the independence assumption. Our goal is to build a language model in which various word relationships can be integrated. In this work, we integrate two types of relationship extracted from WordNet and co-occurrence relationships respectively. The integrated model has been tested on several TREC collections. The results show that our model achieves substantial and significant improvements with respect to the models without these relationships. These results clearly show the benefit of integrating word relationships into language models for IR.

© All rights reserved Gao et al. and/or ACM Press

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Bai, Jing, Song, Dawei, Bruza, Peter, Nie, Jian-Yun and Cao, Guihong (2005): Query expansion using term relationships in language models for information retrieval. In: Herzog, Otthein, Schek, Hans-Jorg and Fuhr, Norbert (eds.) Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management October 31 - November 5, 2005, Bremen, Germany. pp. 688-695.

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Bai, Jing, Nie, Jian-Yun and Cao, Guihong (2005): Integrating Compound Terms in Bayesian Text Classification. In: Skowron, Andrzej, Agrawal, Rakesh, Luck, Michael, Yamaguchi, Takahira, Morizet-Mahoudeaux, Pierre, Liu, Jiming and Zhong, Ning (eds.) 2005 IEEE / WIC / ACM International Conference on Web Intelligence WI 2005 19-22 September, 2005, Compiegne, France. pp. 598-601.

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Bai, Jing, Paradis, Francois and Nie, Jian-Yun (2004): Web-supported Matching and Classification of Business. In: Yao, Jingtao, Raghavan, Vijay V. and Wang, G. Y. (eds.) Proceedings of the 2nd International Workshop on Web-based Support Systems September 20, 2004, Beijing, China. pp. 28-36. http://

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