@inproceedings{rohanian-etal-2019-bridging,
title = "{B}ridging the Gap: {A}ttending to Discontinuity in Identification of Multiword Expressions",
author = "Rohanian, Omid and
Taslimipoor, Shiva and
Kouchaki, Samaneh and
Ha, Le An and
Mitkov, Ruslan",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1275",
doi = "10.18653/v1/N19-1275",
pages = "2692--2698",
abstract = "We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both, through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.",
}
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<abstract>We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both, through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.</abstract>
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%0 Conference Proceedings
%T Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
%A Rohanian, Omid
%A Taslimipoor, Shiva
%A Kouchaki, Samaneh
%A Ha, Le An
%A Mitkov, Ruslan
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F rohanian-etal-2019-bridging
%X We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontinuity, an under-explored aspect that poses a significant challenge to computational treatment of MWEs. Two neural architectures are explored: Graph Convolutional Network (GCN) and multi-head self-attention. GCN leverages dependency parse information, and self-attention attends to long-range relations. We finally propose a combined model that integrates complementary information from both, through a gating mechanism. The experiments on a standard multilingual dataset for verbal MWEs show that our model outperforms the baselines not only in the case of discontinuous MWEs but also in overall F-score.
%R 10.18653/v1/N19-1275
%U https://aclanthology.org/N19-1275
%U https://doi.org/10.18653/v1/N19-1275
%P 2692-2698
Markdown (Informal)
[Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions](https://aclanthology.org/N19-1275) (Rohanian et al., NAACL 2019)
ACL
- Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki, Le An Ha, and Ruslan Mitkov. 2019. Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2692–2698, Minneapolis, Minnesota. Association for Computational Linguistics.