Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three exam...

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Autores principales:	Hamish Cunningham, Valentin Tablan, Angus Roberts, Kalina Bontcheva
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c77e33ff669545708d8e3e0cd966cd45
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spelling	oai:doaj.org-article:c77e33ff669545708d8e3e0cd966cd452021-11-18T05:52:28ZGetting more out of biomedical documents with GATE's full lifecycle open source text analytics.1553-734X1553-735810.1371/journal.pcbi.1002854https://doaj.org/article/c77e33ff669545708d8e3e0cd966cd452013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23408875/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/outcome models in the UK's largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors' own group) who work in text processing for biomedicine and other areas. GATE is available online <1> under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis.Hamish CunninghamValentin TablanAngus RobertsKalina BontchevaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 2, p e1002854 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Hamish Cunningham Valentin Tablan Angus Roberts Kalina Bontcheva Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.
description	This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/outcome models in the UK's largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors' own group) who work in text processing for biomedicine and other areas. GATE is available online <1> under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis.
format	article
author	Hamish Cunningham Valentin Tablan Angus Roberts Kalina Bontcheva
author_facet	Hamish Cunningham Valentin Tablan Angus Roberts Kalina Bontcheva
author_sort	Hamish Cunningham
title	Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.
title_short	Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.
title_full	Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.
title_fullStr	Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.
title_full_unstemmed	Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.
title_sort	getting more out of biomedical documents with gate's full lifecycle open source text analytics.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c77e33ff669545708d8e3e0cd966cd45
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Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.

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