Using social annotations to improve language model for information retrieval

Authors:
Shengliang Xu;Shenghua Bao;Yunbo Cao;Yong Yu
Affiliations:
Shanghai Jiao Tong University, Shanghai, China;Shanghai Jiao Tong University, Shanghai, China;Microsoft Research Asia, Beijing, China;Shanghai Jiao Tong University, Shanghai, China
Venue:
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Year:
2007

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Abstract

This poster is concerned with the problem of exploring the use of social annotations for improving language models for information retrieval (denoted as LMIR). Two properties of social annotations, namely keyword property and structure property are studied for this aim. The keyword property improves LMIR by concatenating all the annotations of a document to generate a summary of the document. The structure property can boost LMIR further when similarity among annotations and similarity among documents are taken into consideration simultaneously. The two properties of social annotations are leveraged for the use of language modeling with a mixture model named as "Language Annotation Model" (denoted as LAM). Evaluations using del.icio.us data show that LAM outperforms the traditional LMIR approaches significantly.