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        <title>Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?</title>
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    <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.</abstract>
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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
%Y Savary, Agata
%Y Ramisch, Carlos
%Y Hwang, Jena D.
%Y Schneider, Nathan
%Y Andresen, Melanie
%Y Pradhan, Sameer
%Y Petruck, Miriam R. L.
%S Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
%D 2018
%8 August
%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, LAW-MWE 2018)

• Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality? (Hakimi Parizi & Cook, LAW-MWE 2018)

ACL

• Ali Hakimi Parizi and Paul Cook. 2018. Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 185–192, Santa Fe, New Mexico, USA. Association for Computational Linguistics.