文章基本信息

标题：Context-Aware Prediction of Derivational Word-forms
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作者：Ekaterina Vylomova ; Ryan Cotterell ; Timothy Baldwin 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：118-124
语种：English
出版社：ACL Anthology
摘要：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.