Query Expansion With Local Conceptual Word Embeddings in Microblog Retrieval

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we focus on the use of local conceptual word embeddings for enhance microblog retrieval effectiveness. In particular, we propose a novel k-Nearest Neighbor (kNN) based Query Expansion (QE) algorithm to generate words from local word embeddings to expand the original query, which leads to better understanding of the information need. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion algorithm, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on the official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.

Automated summary

This paper focuses on enhancing microblog retrieval effectiveness by using local conceptual word embeddings and incorporating strategies such as query expansion and temporal evidences. The proposed approach outperforms baseline methods in terms of understanding information needs and meeting users' real-time information requirements, as demonstrated by experiments on the official TREC Twitter corpora.