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        <title>Predicting Degrees of Technicality in Automatic Terminology Extraction</title>
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        <namePart type="given">Anna</namePart>
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        <namePart type="given">Dominik</namePart>
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        <namePart type="given">Sabine</namePart>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Natalie</namePart>
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            <namePart type="given">Joel</namePart>
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    <abstract>While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on technicality prediction. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general- vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.</abstract>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Predicting Degrees of Technicality in Automatic Terminology Extraction
%A Hätty, Anna
%A Schlechtweg, Dominik
%A Dorna, Michael
%A Schulte im Walde, Sabine
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F hatty-etal-2020-predicting
%X While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on technicality prediction. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general- vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.
%R 10.18653/v1/2020.acl-main.258
%U https://aclanthology.org/2020.acl-main.258
%U https://doi.org/10.18653/v1/2020.acl-main.258
%P 2883-2889

Markdown (Informal)

[Predicting Degrees of Technicality in Automatic Terminology Extraction](https://aclanthology.org/2020.acl-main.258) (Hätty et al., ACL 2020)

• Predicting Degrees of Technicality in Automatic Terminology Extraction (Hätty et al., ACL 2020)

ACL

• Anna Hätty, Dominik Schlechtweg, Michael Dorna, and Sabine Schulte im Walde. 2020. Predicting Degrees of Technicality in Automatic Terminology Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2883–2889, Online. Association for Computational Linguistics.