Short Review on srBERT: Automatic Article Classification Model for Systematic Review Using BERT

Choe, Seon; Aum, Sungmin; Kim, Ju Han

doi:10.53043/2347-3894.acam90025

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transformer architecture is an encoder-decoder model training structure that does not use recurrence and convolutions [17]. Prior work showed that using PLMs can significantly increase effectiveness in ad-hoc search [18] as well as in professional search [19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

Wang¹,

Scells²,

Koopman³

et al. 2022

Preprint

View full text Add to dashboard Cite

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., the Medical Subject Headings (MeSH) thesaurus), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited.This article investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. In this context, we devise lexical and pre-trained language models based methods. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We further contribute an extensive analysis of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

show abstract