@inproceedings{hofmann-etal-2020-graph,
title = "A Graph Auto-encoder Model of Derivational Morphology",
author = {Hofmann, Valentin and
Sch{\"u}tze, Hinrich and
Pierrehumbert, Janet},
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.106/",
doi = "10.18653/v1/2020.acl-main.106",
pages = "1127--1138",
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."
}
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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)
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.