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        <title>Adapting the TTL Romanian POS Tagger to the Biomedical Domain</title>
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        <namePart type="given">Maria</namePart>
        <namePart type="family">Mitrofan</namePart>
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        <namePart type="given">Radu</namePart>
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            <title>Proceedings of the Biomedical NLP Workshop associated with RANLP 2017</title>
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        <name type="personal">
            <namePart type="given">Svetla</namePart>
            <namePart type="family">Boytcheva</namePart>
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        <name type="personal">
            <namePart type="given">Kevin</namePart>
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            <namePart type="family">Cohen</namePart>
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            <namePart type="given">Guergana</namePart>
            <namePart type="family">Savova</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>This paper presents the adaptation of the Hidden Markov Models-based TTL part-of-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97% when TTL’s baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.</abstract>
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%0 Conference Proceedings
%T Adapting the TTL Romanian POS Tagger to the Biomedical Domain
%A Mitrofan, Maria
%A Ion, Radu
%Y Boytcheva, Svetla
%Y Cohen, Kevin Bretonnel
%Y Savova, Guergana
%Y Angelova, Galia
%S Proceedings of the Biomedical NLP Workshop associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F mitrofan-ion-2017-adapting
%X This paper presents the adaptation of the Hidden Markov Models-based TTL part-of-speech tagger to the biomedical domain. TTL is a text processing platform that performs sentence splitting, tokenization, POS tagging, chunking and Named Entity Recognition (NER) for a number of languages, including Romanian. The POS tagging accuracy obtained by the TTL POS tagger exceeds 97% when TTL’s baseline model is updated with training information from a Romanian biomedical corpus. This corpus is developed in the context of the CoRoLa (a reference corpus for the contemporary Romanian language) project. Informative description and statistics of the Romanian biomedical corpus are also provided.
%R 10.26615/978-954-452-044-1_002
%U https://doi.org/10.26615/978-954-452-044-1_002
%P 8-14

Markdown (Informal)

[Adapting the TTL Romanian POS Tagger to the Biomedical Domain](https://doi.org/10.26615/978-954-452-044-1_002) (Mitrofan & Ion, RANLP 2017)

• Adapting the TTL Romanian POS Tagger to the Biomedical Domain (Mitrofan & Ion, RANLP 2017)

ACL

• Maria Mitrofan and Radu Ion. 2017. Adapting the TTL Romanian POS Tagger to the Biomedical Domain. In Proceedings of the Biomedical NLP Workshop associated with RANLP 2017, pages 8–14, Varna, Bulgaria. INCOMA Ltd..