SeCoDa: Sense Complexity Dataset

David Strohmaier; Sian Gooding; Shiva Taslimipoor; Ekaterina Kochmar

SeCoDa: Sense Complexity Dataset

David Strohmaier, Sian Gooding, Shiva Taslimipoor, 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

The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation, SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner’s Dictionary. This way, we can offer more coarse-grained senses than directly available in WordNet.

Original language	English
Title of host publication	Proceedings of the 12th Conference on Language Resources and Evolution
Subtitle of host publication	LREC 2020
Publisher	European Language Resources Association (ELRA)
Pages	5962-5967
Number of pages	6
Publication status	Published - 11 May 2021
Event	LREC 2020: Proceedings of the 12th Conference on Language Resources and Evaluation - Virtual, Marseille, France Duration: 11 May 2020 → 16 May 2020 https://lrec2020.lrec-conf.org/en/

Conference

Conference	LREC 2020
Abbreviated title	LREC 2020
Country/Territory	France
City	Marseille
Period	11/05/20 → 16/05/20
Internet address	https://lrec2020.lrec-conf.org/en/

Access to Document

https://www.aclweb.org/anthology/2020.lrec-1.730.pdfLicence: CC BY-NC

Cite this

@inproceedings{2ee39b907b4747f5931b34c19fbd381f,

title = "SeCoDa: Sense Complexity Dataset",

abstract = "The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation, SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner{\textquoteright}s Dictionary. This way, we can offer more coarse-grained senses than directly available in WordNet.",

author = "David Strohmaier and Sian Gooding and Shiva Taslimipoor and Ekaterina Kochmar",

year = "2021",

month = may,

day = "11",

language = "English",

pages = "5962--5967",

booktitle = "Proceedings of the 12th Conference on Language Resources and Evolution",

publisher = "European Language Resources Association (ELRA)",

note = "LREC 2020 : Proceedings of the 12th Conference on Language Resources and Evaluation, LREC 2020 ; Conference date: 11-05-2020 Through 16-05-2020",

url = "https://lrec2020.lrec-conf.org/en/",

}

TY - GEN

T1 - SeCoDa: Sense Complexity Dataset

AU - Strohmaier, David

AU - Gooding, Sian

AU - Taslimipoor, Shiva

AU - Kochmar, Ekaterina

PY - 2021/5/11

Y1 - 2021/5/11

N2 - The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation, SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner’s Dictionary. This way, we can offer more coarse-grained senses than directly available in WordNet.

AB - The Sense Complexity Dataset (SeCoDa) provides a corpus that is annotated jointly for complexity and word senses. It thus provides a valuable resource for both word sense disambiguation and the task of complex word identification. The intention is that this dataset will be used to identify complexity at the level of word senses rather than word tokens. For word sense annotation, SeCoDa uses a hierarchical scheme that is based on information available in the Cambridge Advanced Learner’s Dictionary. This way, we can offer more coarse-grained senses than directly available in WordNet.

M3 - Chapter in a published conference proceeding

SP - 5962

EP - 5967

BT - Proceedings of the 12th Conference on Language Resources and Evolution

PB - European Language Resources Association (ELRA)

T2 - LREC 2020

Y2 - 11 May 2020 through 16 May 2020

ER -

SeCoDa: Sense Complexity Dataset

Abstract

Conference

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