A scientific information extraction dataset for nature inspired engineering

Ruben Kruiper; Julian F. V. Vincent; Jessica Chen-Burger; Marc P. Y. Desmulliez; Ioannis Konstas

A scientific information extraction dataset for nature inspired engineering

Ruben Kruiper^*, Julian F. V. Vincent, Jessica Chen-Burger, Marc P. Y. Desmulliez, Ioannis Konstas

^*Corresponding author for this work

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

3 Citations (Scopus)

39 Downloads (Pure)

Abstract

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.

Original language	English
Title of host publication	Proceedings of the 12th Language Resources and Evaluation Conference
Publisher	European Language Resources Association
Pages	2078-2085
Number of pages	8
ISBN (Electronic)	9791095546344
Publication status	Published - May 2020
Event	12th International Conference on Language Resources and Evaluation 2020 - Marseille, France Duration: 11 May 2020 → 16 May 2020 https://lrec2020.lrec-conf.org/en/index.html

Conference

Conference	12th International Conference on Language Resources and Evaluation 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/index.html

Keywords

Biomimetics
Relation Extraction
Scientific Information Extraction
Trade-Offs

ASJC Scopus subject areas

Language and Linguistics
Education
Library and Information Sciences
Linguistics and Language

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Cite this

@inproceedings{4e6bb22132f94991946dc0a753bacd5a,

title = "A scientific information extraction dataset for nature inspired engineering",

abstract = "Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.",

keywords = "Biomimetics, Relation Extraction, Scientific Information Extraction, Trade-Offs",

author = "Ruben Kruiper and Vincent, {Julian F. V.} and Jessica Chen-Burger and Desmulliez, {Marc P. Y.} and Ioannis Konstas",

year = "2020",

month = may,

language = "English",

pages = "2078--2085",

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

publisher = "European Language Resources Association",

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

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

}

Kruiper, R, Vincent, JFV, Chen-Burger, J , Desmulliez, MPY & Konstas, I 2020, A scientific information extraction dataset for nature inspired engineering. in Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, pp. 2078-2085, 12th International Conference on Language Resources and Evaluation 2020, Marseille, France, 11/05/20. <https://www.aclweb.org/anthology/2020.lrec-1.255/>

TY - GEN

T1 - A scientific information extraction dataset for nature inspired engineering

AU - Kruiper, Ruben

AU - Vincent, Julian F. V.

AU - Chen-Burger, Jessica

AU - Desmulliez, Marc P. Y.

AU - Konstas, Ioannis

PY - 2020/5

Y1 - 2020/5

N2 - Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.

AB - Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.

KW - Biomimetics

KW - Relation Extraction

KW - Scientific Information Extraction

KW - Trade-Offs

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M3 - Conference contribution

AN - SCOPUS:85096603987

SP - 2078

EP - 2085

BT - Proceedings of the 12th Language Resources and Evaluation Conference

PB - European Language Resources Association

T2 - 12th International Conference on Language Resources and Evaluation 2020

Y2 - 11 May 2020 through 16 May 2020

ER -

A scientific information extraction dataset for nature inspired engineering

Abstract

Conference

Keywords

ASJC Scopus subject areas

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