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        <title>Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection</title>
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        <namePart type="given">Olga</namePart>
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        <namePart type="given">Kristen</namePart>
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        <namePart type="given">Adam</namePart>
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            <namePart type="given">Emily</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>We use a broad coverage, linguistically precise English Resource Grammar (ERG) to detect negation scope in sentences taken from pathology reports. We show that incorporating this information in feature extraction has a positive effect on classification of the reports with respect to cancer laterality compared with NegEx, a commonly used tool for negation detection. We analyze the differences between NegEx and ERG results on our dataset and how these differences indicate some directions for future work.</abstract>
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%0 Conference Proceedings
%T Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection
%A Zamaraeva, Olga
%A Howell, Kristen
%A Rhine, Adam
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F zamaraeva-etal-2018-improving
%X We use a broad coverage, linguistically precise English Resource Grammar (ERG) to detect negation scope in sentences taken from pathology reports. We show that incorporating this information in feature extraction has a positive effect on classification of the reports with respect to cancer laterality compared with NegEx, a commonly used tool for negation detection. We analyze the differences between NegEx and ERG results on our dataset and how these differences indicate some directions for future work.
%U https://aclanthology.org/C18-1302
%P 3564-3575

Markdown (Informal)

[Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection](https://aclanthology.org/C18-1302) (Zamaraeva et al., COLING 2018)

• Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection (Zamaraeva et al., COLING 2018)

ACL

• Olga Zamaraeva, Kristen Howell, and Adam Rhine. 2018. Improving Feature Extraction for Pathology Reports with Precise Negation Scope Detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3564–3575, Santa Fe, New Mexico, USA. Association for Computational Linguistics.