A Guide to Dictionary-Based Text Mining

Helen V. Cook, Lars Juhl Jensen

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskningpeer review

17 Citationer (Scopus)

Abstract

PubMed contains more than 27 million documents, and this number is growing at an estimated 4% per year. Even within specialized topics, it is no longer possible for a researcher to read any field in its entirety, and thus nobody has a complete picture of the scientific knowledge in any given field at any time. Text mining provides a means to automatically read this corpus and to extract the relations found therein as structured information. Having data in a structured format is a huge boon for computational efforts to access, cross reference, and mine the data stored therein. This is increasingly useful as biological research is becoming more focused on systems and multi-omics integration. This chapter provides an overview of the steps that are required for text mining: tokenization, named entity recognition, normalization, event extraction, and benchmarking. It discusses a variety of approaches to these tasks and then goes into detail on how to prepare data for use specifically with the JensenLab tagger. This software uses a dictionary-based approach and provides the text mining evidence for STRING and several other databases.

OriginalsprogEngelsk
TitelBioinformatics and Drug Discovery
RedaktørerRichard S. Larson, Tudor I. Oprea
Antal sider17
Vol/bind1939
ForlagHumana Press
Publikationsdato2019
Udgave3
Sider73-89
ISBN (Trykt) 978-1-4939-9088-7
ISBN (Elektronisk)978-1-4939-9089-4
DOI
StatusUdgivet - 2019
NavnMethods in Molecular Biology
ISSN1064-3745

Citationsformater