@inproceedings{waszczuk-etal-2019-neural,
title = "A Neural Graph-based Approach to Verbal {MWE} Identification",
author = "Waszczuk, Jakub and
Ehren, Rafael and
Stodden, Regina and
Kallmeyer, Laura",
editor = "Savary, Agata and
Escart{\'\i}n, Carla Parra and
Bond, Francis and
Mitrovi{\'c}, Jelena and
Mititelu, Verginica Barbu",
booktitle = "Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5113",
doi = "10.18653/v1/W19-5113",
pages = "114--124",
abstract = "We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018).",
}
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<abstract>We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018).</abstract>
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%0 Conference Proceedings
%T A Neural Graph-based Approach to Verbal MWE Identification
%A Waszczuk, Jakub
%A Ehren, Rafael
%A Stodden, Regina
%A Kallmeyer, Laura
%Y Savary, Agata
%Y Escartín, Carla Parra
%Y Bond, Francis
%Y Mitrović, Jelena
%Y Mititelu, Verginica Barbu
%S Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F waszczuk-etal-2019-neural
%X We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018).
%R 10.18653/v1/W19-5113
%U https://aclanthology.org/W19-5113
%U https://doi.org/10.18653/v1/W19-5113
%P 114-124
Markdown (Informal)
[A Neural Graph-based Approach to Verbal MWE Identification](https://aclanthology.org/W19-5113) (Waszczuk et al., MWE 2019)
ACL
- Jakub Waszczuk, Rafael Ehren, Regina Stodden, and Laura Kallmeyer. 2019. A Neural Graph-based Approach to Verbal MWE Identification. In Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019), pages 114–124, Florence, Italy. Association for Computational Linguistics.