Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality?

Ali Hakimi Parizi, Paul Cook

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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.
Anthology ID:
W18-4920
Volume:
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Agata Savary, Carlos Ramisch, Jena D. Hwang, Nathan Schneider, Melanie Andresen, Sameer Pradhan, Miriam R. L. Petruck
Venues:
LAW | MWE
SIGs:
SIGLEX | SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
185–192
Language:
URL:
https://aclanthology.org/W18-4920
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Do Character-Level Neural Network Language Models Capture Knowledge of Multiword Expression Compositionality? (Hakimi Parizi & Cook, LAW-MWE 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W18-4920.pdf