@inproceedings{liu-etal-2021-lexical,
title = "Lexical Semantic Recognition",
author = "Liu, Nelson F. and
Hershcovich, Daniel and
Kranzlein, Michael and
Schneider, Nathan",
editor = "Cook, Paul and
Mitrovi{\'c}, Jelena and
Escart{\'\i}n, Carla Parra and
Vaidya, Ashwini and
Osenova, Petya and
Taslimipoor, Shiva and
Ramisch, Carlos",
booktitle = "Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mwe-1.6",
doi = "10.18653/v1/2021.mwe-1.6",
pages = "49--56",
abstract = "In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.",
}
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<abstract>In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.</abstract>
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%0 Conference Proceedings
%T Lexical Semantic Recognition
%A Liu, Nelson F.
%A Hershcovich, Daniel
%A Kranzlein, Michael
%A Schneider, Nathan
%Y Cook, Paul
%Y Mitrović, Jelena
%Y Escartín, Carla Parra
%Y Vaidya, Ashwini
%Y Osenova, Petya
%Y Taslimipoor, Shiva
%Y Ramisch, Carlos
%S Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F liu-etal-2021-lexical
%X In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.
%R 10.18653/v1/2021.mwe-1.6
%U https://aclanthology.org/2021.mwe-1.6
%U https://doi.org/10.18653/v1/2021.mwe-1.6
%P 49-56
Markdown (Informal)
[Lexical Semantic Recognition](https://aclanthology.org/2021.mwe-1.6) (Liu et al., MWE 2021)
ACL
- Nelson F. Liu, Daniel Hershcovich, Michael Kranzlein, and Nathan Schneider. 2021. Lexical Semantic Recognition. In Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021), pages 49–56, Online. Association for Computational Linguistics.