Semantic Sequential Query Expansion for Biomedical Article Search

Abstract

The conventional sequential dependence model (SDM) has been proved to perform better than the bag of words model for biomedical article search because it pays attention to the sequence information within queries. Meanwhile, introducing lexical semantic relations into query expansion becomes a hot topic in IR research. However, a few research have been conducted on combining semantic and sequence information together. Hence, we propose the semantic sequential dependence model in this paper, which provides an innovative combination of semantic information and the conventional SDM. Specifically, our synonyms are obtained automatically through the word embeddings which are trained on the domain-specific corpus by selecting an appropriate language model. Then, these synonyms are utilized to generate possible sequences with the same semantics as the original query and these sequences are fed into SDM to obtain the final retrieval results. The proposed approach is evaluated on 2016 and 2017 BioASQ benchmark test sets and the experimental results show that our query expansion approach outperforms the baseline and other participants in the BioASQ competitions.

Keywords

• Sequential dependence model
• semantic query expansion
• semantic sequential dependence model
• word embedding