Abstract
In this paper we address the problem of entity search. Expert search and time search are used as examples. In entity search, given a query and an entity type, a search system returns a ranked list of entities in the type (e.g., person name, time expression) relevant to the query. Ranking is a key issue in entity search. In the literature, only expert search was studied and the use of co-occurrence was proposed. In general, many features may be useful for ranking in entity search. We propose using a linear model to combine the uses of different features and employing a supervised learning approach in training of the model. Experimental results on several data sets indicate that our method significantly outperforms the baseline method based solely on co-occurrences.
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Hu, G., Liu, J., Li, H., Cao, Y., Nie, JY., Gao, J. (2006). A Supervised Learning Approach to Entity Search. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_5
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DOI: https://doi.org/10.1007/11880592_5
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