Text Retrieval for Systematic Reviews

Abstract

The past decade has seen an exponential growth in the amount of information published on the web. This increase in the amount of data published is directly proportional to the steady growth in the number of data publishing sources. As a result one of the oldest problems in Information Retrieval(IR) the “Vocabulary Mismatch problem” has taken center stage. Over the years a lot of techniques have been proposed to tackle the Vocabulary Mismatch problem faced in various domains. In this work we explore the utility of the different types of latent semantic models for retrieval purposes in the med- ical domain. Our work focuses on the process of Systematic Reviews 2.3 . Systematic reviews is a high-recall oriented process i.e recall is of higher precedence as compared to precision for this system. An ideal retrieval scenario for Systematic Reviews is the retrieval of all relevant articles at the top of the rankings. The following work is a comparative study of three semantic techniques namely Latent Dirichlet Allocation, Relevance-based Language Models and Query expansion using OvidSP’s [5] inbuilt Basic search utility to explore the possibility of obtaining a high-recall system without compromising much on precision. It was noted that pseudo-relevance feedback(PRF) based algorithms showed relatively better performance as compared to the other tech- niques mentioned above. It was also noted that the PRF based algorithms performed better than the OvidSP Natural Language(NL) search which can be considered as the current standard in NL search for medical literatur