Deep Learning Models For Multiword Expression Identification

Waseem Gharbieh, Virendrakumar Bhavsar, Paul Cook


Abstract
Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.
Anthology ID:
S17-1006
Volume:
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
54–64
Language:
URL:
https://www.aclweb.org/anthology/S17-1006
DOI:
10.18653/v1/S17-1006
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-1006.pdf