At Clippers Tuesday, Manirupa will present “A Phrasal Embedding–based General Language Model for Query Expansion in Information Retrieval”:
Traditional knowledge graphs driven by knowledge bases can represent facts about and capture relationships among entities very well, thus performing quite accurately in factual information retrieval. However, in addressing the complex information needs of subjective queries requiring adaptive decision support, these systems can fall short as they are not able to fully capture novel associations among potentially key concepts. In this work, we explore a novel use of language model–based document ranking to develop a fully unsupervised method for query expansion by associating documents with novel related concepts extracted from the text. To achieve this we extend the word embedding-based generalized language model due to Ganguly et al. (2015) to employ phrasal embeddings, and evaluate its performance on an IR task using the TREC 2016 clinical decision support challenge dataset. Our model, used for query expansion both directly and via feedback loop, shows statistically significant improvement not just over various baselines utilizing standard MeSH terms and UMLS concepts for query expansion (Rivas et al., 2014), but also over our word embedding-based language model baseline, built on top of a standard Okapi BM25 based document retrieval system.