Leveraging External Knowledge to Enhance Query Model for Event Query
Retrieval based on event query has recently become one of the most popular applications in information retrieval domain, whose goal is to retrieve event-related documents according to the given query about some specific event. However, using conventional retrieval method for this kind of task would usually be demonstrated with poor performance. To enhance query model and improve retrieval effectiveness for event query, an adaptive learning approach of PLSA model is presented in this paper. Through leveraging the knowledge of known coarse-grained events from external resource, the new approach can adaptively adjust the topic generative process of PLSA model on pseudo-relevance feedback documents, and learn the accurate language model for a particular topic, i.e., target event, which can be used to update the representation of users intention and finally improve the retrieval results. Experimental results on standard TREC collections show the proposed approach consistently outperform the state-of-the-art methods.
KeywordsLanguage model Event-oriented retrieval Pseudo relevance feedback Topic model
This work was supported by the National Natural Science Foundation of China (61572494, 61462027) and the fund project of Jiangxi Province Education Office (GJJ160529).
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