Multi-Task Learning of Keyphrase Boundary Classification

Isabelle Augenstein; Anders Søgaard

doi:10.18653/v1/p17-2054

Multi-Task Learning of Keyphrase Boundary Classification

Isabelle Augenstein, Anders Søgaard

Department of Computer Science

20 Citations (Scopus)

Abstract

Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far un-derexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.

Original language	English
Title of host publication	Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics : (Short Papers)
Editors	Regina Barzilay, Min-Yen Kan
Number of pages	6
Volume	2
Publisher	Association for Computational Linguistics
Publication date	2017
Pages	341-346
ISBN (Print)	978-1-945626-76-0
DOIs	https://doi.org/10.18653/v1/p17-2054
Publication status	Published - 2017
Event	Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) - Vancouver, Canada Duration: 1 Jul 2017 → 1 Jul 2017

Conference

Conference	Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Location	Vancouver, Canada
Period	01/07/2017 → 01/07/2017

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Multi-Task Learning of Keyphrase Boundary Classification. / Augenstein, Isabelle ; Søgaard, Anders.

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics : (Short Papers). ed. / Regina Barzilay; Min-Yen Kan. Vol. 2 Association for Computational Linguistics, 2017. p. 341-346.

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

Augenstein, I & Søgaard, A 2017, Multi-Task Learning of Keyphrase Boundary Classification. in R Barzilay & M-Y Kan (eds), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics : (Short Papers). vol. 2, Association for Computational Linguistics, pp. 341-346, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 01/07/2017. https://doi.org/10.18653/v1/p17-2054

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abstract = "Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practice, this task is so far un-derexplored, partly due to the lack of labelled data. To overcome this, we explore several auxiliary tasks, including semantic super-sense tagging and identification of multi-word expressions, and cast the task as a multi-task learning problem with deep recurrent neural networks. Our multi-task models perform significantly better than previous state of the art approaches on two scientific KBC datasets, particularly for long keyphrases.",

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M3 - Article in proceedings

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EP - 346

BT - Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics

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Multi-Task Learning of Keyphrase Boundary Classification

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