Emerald 110k: a multidisciplinary dataset for abstract sentence classification

Connor Stead; Stephen Smith; Peter Busch; Savanid Vatanasakdakul

Emerald 110k: a multidisciplinary dataset for abstract sentence classification

Connor Stead, Stephen Smith, Peter Busch, Savanid Vatanasakdakul

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

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Abstract

Background: Datasets available for abstract sentence classification modelling are predominately comprised of abstracts sourced from biomedical research.

Aims: To contribute a large non-biomedical multidisciplinary dataset for abstract sentence classification model research.

Method: Bulk extract and transformation of Emerald Group Publishing structured abstracts indexed on Scopus.

Results: We present the largest multidisciplinary dataset for abstract sentence classification modelling, consisting of 1,050,397 sentences from 103,457 abstracts.

Original language	English
Title of host publication	Proceedings of the 17th Workshop of the Australasian Language Technology Association
Editors	Meladel Mistica, Massimo Piccardi, Andrew MacKinlay
Place of Publication	Melbourne, VIC
Publisher	Australasian Language Technology Association
Pages	120-125
Number of pages	6
Publication status	Published - 2019
Event	17th Annual Workshop of The Australasian Language Technology Association (ALTA 2019) - Sydney, Australia Duration: 4 Dec 2019 → 6 Dec 2019

Conference

Conference	17th Annual Workshop of The Australasian Language Technology Association (ALTA 2019)
Country/Territory	Australia
City	Sydney
Period	4/12/19 → 6/12/19

Bibliographical note

Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Structured abstracts
Natural language processing
information systems

Access to Document

Publisher version (open access)Final published version, 240 KBLicence: CC BY

https://www.aclweb.org/anthology/U19-1016Licence: CC BY

Cite this

Stead, C., Smith, S., Busch, P., & Vatanasakdakul, S. (2019). Emerald 110k: a multidisciplinary dataset for abstract sentence classification. In M. Mistica, M. Piccardi, & A. MacKinlay (Eds.), Proceedings of the 17th Workshop of the Australasian Language Technology Association (pp. 120-125). Australasian Language Technology Association. https://www.aclweb.org/anthology/U19-1016

@inproceedings{d9763441cdde486e999856906a4ddd09,

title = "Emerald 110k: a multidisciplinary dataset for abstract sentence classification",

abstract = "Background: Datasets available for abstract sentence classification modelling are predominately comprised of abstracts sourced from biomedical research. Aims: To contribute a large non-biomedical multidisciplinary dataset for abstract sentence classification model research. Method: Bulk extract and transformation of Emerald Group Publishing structured abstracts indexed on Scopus. Results: We present the largest multidisciplinary dataset for abstract sentence classification modelling, consisting of 1,050,397 sentences from 103,457 abstracts. ",

keywords = "Structured abstracts, Natural language processing, information systems",

author = "Connor Stead and Stephen Smith and Peter Busch and Savanid Vatanasakdakul",

note = "Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. ; 17th Annual Workshop of The Australasian Language Technology Association (ALTA 2019) ; Conference date: 04-12-2019 Through 06-12-2019",

year = "2019",

language = "English",

pages = "120--125",

editor = "Meladel Mistica and Massimo Piccardi and Andrew MacKinlay",

booktitle = "Proceedings of the 17th Workshop of the Australasian Language Technology Association",

publisher = "Australasian Language Technology Association",

}

Stead, C, Smith, S , Busch, P & Vatanasakdakul, S 2019, Emerald 110k: a multidisciplinary dataset for abstract sentence classification. in M Mistica, M Piccardi & A MacKinlay (eds), Proceedings of the 17th Workshop of the Australasian Language Technology Association. Australasian Language Technology Association, Melbourne, VIC, pp. 120-125, 17th Annual Workshop of The Australasian Language Technology Association (ALTA 2019), Sydney, New South Wales, Australia, 4/12/19. <https://www.aclweb.org/anthology/U19-1016>

Emerald 110k: a multidisciplinary dataset for abstract sentence classification. / Stead, Connor; Smith, Stephen ; Busch, Peter et al.
Proceedings of the 17th Workshop of the Australasian Language Technology Association. ed. / Meladel Mistica; Massimo Piccardi; Andrew MacKinlay. Melbourne, VIC: Australasian Language Technology Association, 2019. p. 120-125.

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

TY - GEN

T1 - Emerald 110k

T2 - 17th Annual Workshop of The Australasian Language Technology Association (ALTA 2019)

AU - Stead, Connor

AU - Smith, Stephen

AU - Busch, Peter

AU - Vatanasakdakul, Savanid

N1 - Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2019

Y1 - 2019

N2 - Background: Datasets available for abstract sentence classification modelling are predominately comprised of abstracts sourced from biomedical research. Aims: To contribute a large non-biomedical multidisciplinary dataset for abstract sentence classification model research. Method: Bulk extract and transformation of Emerald Group Publishing structured abstracts indexed on Scopus. Results: We present the largest multidisciplinary dataset for abstract sentence classification modelling, consisting of 1,050,397 sentences from 103,457 abstracts.

AB - Background: Datasets available for abstract sentence classification modelling are predominately comprised of abstracts sourced from biomedical research. Aims: To contribute a large non-biomedical multidisciplinary dataset for abstract sentence classification model research. Method: Bulk extract and transformation of Emerald Group Publishing structured abstracts indexed on Scopus. Results: We present the largest multidisciplinary dataset for abstract sentence classification modelling, consisting of 1,050,397 sentences from 103,457 abstracts.

KW - Structured abstracts

KW - Natural language processing

KW - information systems

M3 - Conference proceeding contribution

SP - 120

EP - 125

BT - Proceedings of the 17th Workshop of the Australasian Language Technology Association

A2 - Mistica, Meladel

A2 - Piccardi, Massimo

A2 - MacKinlay, Andrew

PB - Australasian Language Technology Association

CY - Melbourne, VIC

Y2 - 4 December 2019 through 6 December 2019

ER -

Emerald 110k: a multidisciplinary dataset for abstract sentence classification

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Cite this