Neural Networks for Multi-Word Expression Detection

Natalia Klyueva, Antoine Doucet, Milan Straka


Abstract
In this paper we describe the MUMULS system that participated to the 2017 shared task on automatic identification of verbal multiword expressions (VMWEs). The MUMULS system was implemented using a supervised approach based on recurrent neural networks using the open source library TensorFlow. The model was trained on a data set containing annotated VMWEs as well as morphological and syntactic information. The MUMULS system performed the identification of VMWEs in 15 languages, it was one of few systems that could categorize VMWEs type in nearly all languages.
Anthology ID:
W17-1707
Volume:
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
Month:
April
Year:
2017
Address:
Valencia, Spain
Venues:
MWE | WS
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–65
Language:
URL:
https://www.aclweb.org/anthology/W17-1707
DOI:
10.18653/v1/W17-1707
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-1707.pdf