A Graph Auto-encoder Model of Derivational Morphology

被引：0
|
作者：
Hofmann, Valentin [1 ,3 ]
Schutze, Hinrich [3 ]
Pierrehumbert, Janet B. [1 ,2 ]
机构：
[1] Univ Oxford, Fac Linguist, Oxford, England
[2] Univ Oxford, Dept Engn Sci, Oxford, England
[3] Ludwig Maximilians Univ Munchen, Ctr Informat & Language Proc, Munich, Germany
来源：
58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020) | 2020年
基金：
英国艺术与人文研究理事会; 欧洲研究理事会;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics (Bauer, 2019). 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.
引用
收藏
页码：1127 / 1138
页数：12
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