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        <title>A Graph Auto-encoder Model of Derivational Morphology</title>
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        <namePart type="given">Valentin</namePart>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Joyce</namePart>
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    <abstract>There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics. We present a graph auto-encoder that learns embeddings capturing information about the compatibility of affixes and stems in derivation. The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.</abstract>
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%0 Conference Proceedings
%T A Graph Auto-encoder Model of Derivational Morphology
%A Hofmann, Valentin
%A Schütze, Hinrich
%A Pierrehumbert, Janet
%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 hofmann-etal-2020-graph
%X There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics. We present a graph auto-encoder that learns embeddings capturing information about the compatibility of affixes and stems in derivation. The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon.
%R 10.18653/v1/2020.acl-main.106
%U https://aclanthology.org/2020.acl-main.106
%U https://doi.org/10.18653/v1/2020.acl-main.106
%P 1127-1138

Markdown (Informal)

[A Graph Auto-encoder Model of Derivational Morphology](https://aclanthology.org/2020.acl-main.106) (Hofmann et al., ACL 2020)

• A Graph Auto-encoder Model of Derivational Morphology (Hofmann et al., ACL 2020)

ACL

• Valentin Hofmann, Hinrich Schütze, and Janet Pierrehumbert. 2020. A Graph Auto-encoder Model of Derivational Morphology. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1127–1138, Online. Association for Computational Linguistics.