An Analysis of Indexing and Querying Strategies on a Technologically Assisted Review Task

TL;DR

This study evaluates various document indexing and query parsing methods on PubMed data using the CLEF 2017 eHealth collection, highlighting the impact of multi-field indexing on retrieval effectiveness.

Contribution

It explores the effectiveness of different indexing and query parsing techniques for PubMed documents using established IR tools, providing insights into optimizing biomedical information retrieval.

Findings

Including more fields in the PubMed index improves retrieval effectiveness.

Multi-field indexing significantly enhances system performance.

Experimentation with different parsing techniques informs better query formulation.

Abstract

This paper presents a preliminary experimentation study using the CLEF 2017 eHealth Task 2 collection for evaluating the effectiveness of different indexing methodologies of documents and query parsing techniques. Furthermore, it is an attempt to advance and share the efforts of observing the characteristics and helpfulness of various methodologies for indexing PubMed documents and for different topic parsing techniques to produce queries. For this purpose, my research includes experimentation with different document indexing methodologies, by utilising existing tools, such as the Lucene4IR (L4IR) information retrieval system, the Technology Assisted Reviews for Empirical Medicine tool for parsing topics of the CLEF collection and the TREC evaluation tool to appraise system's performance. The results showed that including a greater number of fields to the PubMed indexer of L4IR is a decisive factor for the retrieval effectiveness of L4IR.