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        <title>Enhancing biomedical word embeddings by retrofitting to verb clusters</title>
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        <namePart type="given">Billy</namePart>
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        <namePart type="given">Simon</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Anna</namePart>
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            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
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            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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            <namePart type="given">Kevin</namePart>
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            <namePart type="family">Cohen</namePart>
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            <namePart type="family">Ananiadou</namePart>
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        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Verbs play a fundamental role in many biomed-ical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes. In this work, we show that by using semantic clusters for verbs, a large lexicon of verbclasses derived from biomedical literature, weare able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely-available “off-the-shelf” retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.</abstract>
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%0 Conference Proceedings
%T Enhancing biomedical word embeddings by retrofitting to verb clusters
%A Chiu, Billy
%A Baker, Simon
%A Palmer, Martha
%A Korhonen, Anna
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F chiu-etal-2019-enhancing
%X Verbs play a fundamental role in many biomed-ical tasks and applications such as relation and event extraction. We hypothesize that performance on many downstream tasks can be improved by aligning the input pretrained embeddings according to semantic verb classes. In this work, we show that by using semantic clusters for verbs, a large lexicon of verbclasses derived from biomedical literature, weare able to improve the performance of common pretrained embeddings in downstream tasks by retrofitting them to verb classes. We present a simple and computationally efficient approach using a widely-available “off-the-shelf” retrofitting algorithm to align pretrained embeddings according to semantic verb clusters. We achieve state-of-the-art results on text classification and relation extraction tasks.
%R 10.18653/v1/W19-5014
%U https://aclanthology.org/W19-5014
%U https://doi.org/10.18653/v1/W19-5014
%P 125-134

Markdown (Informal)

[Enhancing biomedical word embeddings by retrofitting to verb clusters](https://aclanthology.org/W19-5014) (Chiu et al., BioNLP 2019)

• Enhancing biomedical word embeddings by retrofitting to verb clusters (Chiu et al., BioNLP 2019)

ACL

• Billy Chiu, Simon Baker, Martha Palmer, and Anna Korhonen. 2019. Enhancing biomedical word embeddings by retrofitting to verb clusters. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 125–134, Florence, Italy. Association for Computational Linguistics.