Complex Word Identification as a Sequence Labelling Task

Sian Gooding; Ekaterina Kochmar

Complex Word Identification as a Sequence Labelling Task

Sian Gooding, Ekaterina Kochmar

Department of Computer Science

University of Cambridge

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Abstract

Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve text simplification. However, most CWI systems to date address the task on a word-by-word basis, not taking the context into account. In this paper, we present a novel approach to CWI based on sequence modelling. Our system is capable of performing CWI in context, does not require extensive feature engineering and outperforms state-of-the-art systems on this task.

Original language	English
Title of host publication	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: ACL 2019
Publisher	ACLWEB.ORG
Pages	1148–1153
Number of pages	5
Publication status	Published - 28 Jul 2019
Event	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: ACL 2019 - Florence, Florence, Italy Duration: 28 Jul 2019 → 2 Aug 2019 https://acl2019.org/EN/index.xhtml.html

Conference

Conference	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2019
Country/Territory	Italy
City	Florence
Period	28/07/19 → 2/08/19
Internet address	https://acl2019.org/EN/index.xhtml.html

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https://www.aclweb.org/anthology/P19-1109.pdfLicence: CC BY

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Gooding, S & Kochmar, E 2019, Complex Word Identification as a Sequence Labelling Task. in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: ACL 2019. ACLWEB.ORG, pp. 1148–1153, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28/07/19. <https://www.aclweb.org/anthology/P19-1109.pdf>

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title = "Complex Word Identification as a Sequence Labelling Task",

abstract = "Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve text simplification. However, most CWI systems to date address the task on a word-by-word basis, not taking the context into account. In this paper, we present a novel approach to CWI based on sequence modelling. Our system is capable of performing CWI in context, does not require extensive feature engineering and outperforms state-of-the-art systems on this task.",

author = "Sian Gooding and Ekaterina Kochmar",

year = "2019",

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language = "English",

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booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: ACL 2019",

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note = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics : ACL 2019, ACL 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

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AU - Kochmar, Ekaterina

PY - 2019/7/28

Y1 - 2019/7/28

N2 - Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve text simplification. However, most CWI systems to date address the task on a word-by-word basis, not taking the context into account. In this paper, we present a novel approach to CWI based on sequence modelling. Our system is capable of performing CWI in context, does not require extensive feature engineering and outperforms state-of-the-art systems on this task.

AB - Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve text simplification. However, most CWI systems to date address the task on a word-by-word basis, not taking the context into account. In this paper, we present a novel approach to CWI based on sequence modelling. Our system is capable of performing CWI in context, does not require extensive feature engineering and outperforms state-of-the-art systems on this task.

M3 - Chapter in a published conference proceeding

SP - 1148

EP - 1153

BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: ACL 2019

PB - ACLWEB.ORG

T2 - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Y2 - 28 July 2019 through 2 August 2019

ER -

Complex Word Identification as a Sequence Labelling Task

Abstract

Conference

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