Lexical Semantic Recognition

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Lexical Semantic Recognition. / Liu, Nelson F.; Hershcovich, Daniel; Kranzlein, Michael; Schneider, Nathan.

Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021). Association for Computational Linguistics, 2021. p. 49-56.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Liu, NF, Hershcovich, D, Kranzlein, M & Schneider, N 2021, Lexical Semantic Recognition. in Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021). Association for Computational Linguistics, pp. 49-56, 17th Workshop on Multiword Expressions (MWE 2021), Online, 06/08/2021. https://doi.org/10.18653/v1/2021.mwe-1.6

APA

Liu, N. F., Hershcovich, D., Kranzlein, M., & Schneider, N. (2021). Lexical Semantic Recognition. In Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021) (pp. 49-56). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.mwe-1.6

Vancouver

Liu NF, Hershcovich D, Kranzlein M, Schneider N. Lexical Semantic Recognition. In Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021). Association for Computational Linguistics. 2021. p. 49-56 https://doi.org/10.18653/v1/2021.mwe-1.6

Author

Liu, Nelson F. ; Hershcovich, Daniel ; Kranzlein, Michael ; Schneider, Nathan. / Lexical Semantic Recognition. Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021). Association for Computational Linguistics, 2021. pp. 49-56

Bibtex

@inproceedings{0cf2292a7c994a35b1ca81c61c5f85cb,
title = "Lexical Semantic Recognition",
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.",
author = "Liu, {Nelson F.} and Daniel Hershcovich and Michael Kranzlein and Nathan Schneider",
year = "2021",
doi = "10.18653/v1/2021.mwe-1.6",
language = "English",
pages = "49--56",
booktitle = "Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)",
publisher = "Association for Computational Linguistics",
note = "17th Workshop on Multiword Expressions (MWE 2021) ; Conference date: 06-08-2021 Through 06-08-2021",

}

RIS

TY - GEN

T1 - Lexical Semantic Recognition

AU - Liu, Nelson F.

AU - Hershcovich, Daniel

AU - Kranzlein, Michael

AU - Schneider, Nathan

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.18653/v1/2021.mwe-1.6

DO - 10.18653/v1/2021.mwe-1.6

M3 - Article in proceedings

SP - 49

EP - 56

BT - Proceedings of the 17th Workshop on Multiword Expressions (MWE 2021)

PB - Association for Computational Linguistics

T2 - 17th Workshop on Multiword Expressions (MWE 2021)

Y2 - 6 August 2021 through 6 August 2021

ER -

ID: 300916255