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        <title>Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically</title>
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        <namePart type="given">Nicholas</namePart>
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            <title>Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022</title>
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            <namePart type="given">Sameer</namePart>
            <namePart type="family">Pradhan</namePart>
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            <namePart type="given">Sandra</namePart>
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    <abstract>This paper presents a method for semi-automatically building a corpus of full-text English-language biomedical articles annotated with part-of-speech tags. The outcomes are a semi-automatic procedure to create a large silver standard corpus of 5 million sentences drawn from a large corpus of full-text biomedical articles annotated for part-of-speech, and a robust, easy-to-use software tool that assists the investigation of differences in two tagged datasets. The method to build the corpus uses two part-of-speech taggers designed to tag biomedical abstracts followed by a human dispute settlement when the two taggers differ on the tagging of a token. The dispute resolution aspect is facilitated by the software tool which organizes and presents the disputed tags. The corpus and all of the software that has been implemented for this study are made publicly available.</abstract>
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%0 Conference Proceedings
%T Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically
%A Elder, Nicholas
%A Mercer, Robert E.
%A Singha Roy, Sudipta
%Y Pradhan, Sameer
%Y Kuebler, Sandra
%S Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F elder-etal-2022-building
%X This paper presents a method for semi-automatically building a corpus of full-text English-language biomedical articles annotated with part-of-speech tags. The outcomes are a semi-automatic procedure to create a large silver standard corpus of 5 million sentences drawn from a large corpus of full-text biomedical articles annotated for part-of-speech, and a robust, easy-to-use software tool that assists the investigation of differences in two tagged datasets. The method to build the corpus uses two part-of-speech taggers designed to tag biomedical abstracts followed by a human dispute settlement when the two taggers differ on the tagging of a token. The dispute resolution aspect is facilitated by the software tool which organizes and presents the disputed tags. The corpus and all of the software that has been implemented for this study are made publicly available.
%U https://aclanthology.org/2022.law-1.16
%P 129-138

Markdown (Informal)

[Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically](https://aclanthology.org/2022.law-1.16) (Elder et al., LAW 2022)

• Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically (Elder et al., LAW 2022)

ACL

• Nicholas Elder, Robert E. Mercer, and Sudipta Singha Roy. 2022. Building a Biomedical Full-Text Part-of-Speech Corpus Semi-Automatically. In Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, pages 129–138, Marseille, France. European Language Resources Association.