ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis

Rodrique Kafando,Maguelonne Teisseire,Rémy Decoupes,Mathieu Roche,Sarah Valentin,Lucile Sautot

doi:10.1007/s13755-021-00156-6

Abstract

Here, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.

Highlights

The usefulness of terminology extraction from corpora is clearly acknowledged as it has generated a great deal of research and discussion
Term extraction strategies are based on combinations of linguistic, statistical measures, and corpus segmentation approaches, while analysis strategies are based on combinations of extracted terms
The free term extraction approach and The driven term extraction approach, we propose a workflow in Fig. 2 for term extraction and analysis dedicated to scientific papers

Summary

Introduction

The usefulness of terminology extraction from corpora is clearly acknowledged as it has generated a great deal of research and discussion. This well-established process is used in natural language processing and has led to the development of several tailored tools such as TBXTools [31], TermSuite [9], BioTex [22], etc. Based on [22], our proposal deals with domain-based terminology extraction from heterogeneous corpora, and how to efficiently generate a quantitative and qualitative analysis. To this end, we propose a generic methodology hinged on a combination of extraction and analysis strategies. Term extraction strategies are based on combinations of linguistic, statistical measures, and corpus segmentation approaches, while analysis strategies are based on combinations of extracted terms.

Methods

Results

Conclusion