Information Retrieval
Information Retrieval (IR) is the science of searching and making sense of information. It synthesizes topics from computer science, mathematics, linguistics, and psychology. As the amount of digital content continues to grow, there is an increased need to be able to effectively and efficiently search, organize, and understand it. While Web search engines (e.g., Bing, Google, and Yandex) are the best-known IR applications, there are many other areas to which IR can be applied. These include targeted advertising, recommender systems (e.g., Amazon user suggestions), cross-lingual search, and spam filtering.
IR is a powerful tool in text analysis. We use the IR techniques for natural language understanding, language generation, and dialogue analysis.
Publications
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Full TextTracking Dragon-Hunters with Language Models. In: Yu PS, Tsotras V, Fox E, Liu B, editors. Proceedings of the 15th Conference on Information and Knowledge Management (CIKM). Arlington, VA: ACM Press. NY, USA; 2006. p. 698-707.
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Full TextContext Features in Email Archives. In: Proceedings of IRiX workshop at SIGIR'05.; 2005.
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Full TextEmail is a stage: discovering people roles from email archives. In: Sanderson M, Järvelin PK, Allan J, Bruza P, editors. Proceedings of 27th annual international ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'04). Sheffield, United Kingdom: ACM Press. NY, USA; 2004. p. 502-3.
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Full TextInteractive Information Retrieval Using Clustering and Spatial Proximity. User Modeling and User Adapted Interaction (UMUAI). 2004;14:259-88.
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Full TextProtecting the Privacy of Observable Behavior in Distributed Recommender Systems. In: Proceedings of SIGIR'03 Workshop on Implicit Measures of User Interests and Preferences.; 2003.
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Full TextMaking MIRACLEs: Interactive Translingual Search for Cebuano and Hindi. ACM Transactions on Asian Language Information Processing (TALIP). 2003;2:219-44.