@inproceedings{vylomova-etal-2017-context,
title = "Context-Aware Prediction of Derivational Word-forms",
author = "Vylomova, Ekaterina and
Cotterell, Ryan and
Baldwin, Timothy and
Cohn, Trevor",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2019",
pages = "118--124",
abstract = "Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose a new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder-decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under lexicon agnostic setting.",
}
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<abstract>Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose a new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder-decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under lexicon agnostic setting.</abstract>
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%0 Conference Proceedings
%T Context-Aware Prediction of Derivational Word-forms
%A Vylomova, Ekaterina
%A Cotterell, Ryan
%A Baldwin, Timothy
%A Cohn, Trevor
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F vylomova-etal-2017-context
%X Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose a new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder-decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under lexicon agnostic setting.
%U https://aclanthology.org/E17-2019
%P 118-124
Markdown (Informal)
[Context-Aware Prediction of Derivational Word-forms](https://aclanthology.org/E17-2019) (Vylomova et al., EACL 2017)
ACL
- Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin, and Trevor Cohn. 2017. Context-Aware Prediction of Derivational Word-forms. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 118–124, Valencia, Spain. Association for Computational Linguistics.