A Supervised Learning Approach to Entity Search
نویسندگان
چکیده
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 cooccurrence 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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