@inproceedings{hakimi-parizi-cook-2018-character,
title = "Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?",
author = "Hakimi Parizi, Ali and
Cook, Paul",
booktitle = "Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions ({LAW}-{MWE}-{C}x{G}-2018)",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-4920",
pages = "185--192",
abstract = "In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.",
}
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%0 Conference Proceedings
%T Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?
%A Hakimi Parizi, Ali
%A Cook, Paul
%S Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
%D 2018
%8 aug
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F hakimi-parizi-cook-2018-character
%X In this paper, we propose the first model for multiword expression (MWE) compositionality prediction based on character-level neural network language models. Experimental results on two kinds of MWEs (noun compounds and verb-particle constructions) and two languages (English and German) suggest that character-level neural network language models capture knowledge of multiword expression compositionality, in particular for English noun compounds and the particle component of English verb-particle constructions. In contrast to many other approaches to MWE compositionality prediction, this character-level approach does not require token-level identification of MWEs in a training corpus, and can potentially predict the compositionality of out-of-vocabulary MWEs.
%U https://aclanthology.org/W18-4920
%P 185-192
Markdown (Informal)
[Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?](https://aclanthology.org/W18-4920) (Hakimi Parizi & Cook, 2018)
ACL