Query Expansion Based on a Feedback Concept Model for Microblog Retrieval

Yashen Wang, Heyan Huang, Chong Feng

Abstract: We tackle the problem of improving microblog retrieval algorithms by proposing a Feedback Concept Model for query expansion. In particular, we expand the query using knowledge information derived from Probase so that the expanded one could better reflect users' search intent, which allows for microblog retrieval at a concept-level, rather than term-level. In the proposed feedback concept model: (i) we mine the concept information implicit in short-texts based on the external knowledge bases; (ii) with the relevant concepts associated with short-texts, a mixture model is generated to estimate a concept language model; (iii) finally, we utilize the concept language model for query expansion. Moreover, we incorporate temporal prior into the proposed query expansion method to satisfy real-time information need. Finally, we test the generalization power of the feedback concept model on the TREC Microblog corpora. The experimental results demonstrate that the proposed model outperforms the previous methods for microblog retrieval significantly.