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:
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W18-4920.pdf