Time-Aware latent concept expansion for microblog search

Abstract

Incorporating the temporal property of words into query expansion methods based on relevance feedback has been shown to have a significant positive effect on microblog search. In contrast to such word-based query expansion methods, we propose a concept-based query expansion method based on a temporal relevance model that uses the temporal variation of concepts (e.g., terms and phrases) on microblogs. Our model naturally extends an extremely effective existing concept-based relevance model by tracking the concept frequency over time. Moreover, the proposed model produces important concepts that are frequently used within a particular time period associated with a given topic, which better discriminate between relevant and non-relevant microblog documents than words. Our experiments using a corpus of microblog data (Tweets2011 corpus) show that the proposed concept-based query expansion method improves search performance significantly, especially for highly relevant documents..