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Methods Mol Biol. 2019;1939:73-89. doi: 10.1007/978-1-4939-9089-4_5.

A Guide to Dictionary-Based Text Mining.

Author information

1
School of Clinical Medicine, University of Cambridge, Cambridge, UK.
2
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
3
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. lars.juhl.jensen@cpr.ku.dk.

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.

KEYWORDS:

Automated text processing; Dictionary-based approach; Named entity recognition; PubMed; Structured information; Text mining; Text normalization

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