Supervised open information extraction

Gabriel Stanovsky; Julian Michael; Luke Zettlemoyer; Ido Dagan

Supervised open information extraction

Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, Ido Dagan

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

180 Scopus citations

Abstract

We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available, 1 outperforms the state-of-The-Art in Open IE on benchmark datasets.

Original language	American English
Title of host publication	Long Papers
Publisher	Association for Computational Linguistics (ACL)
Pages	885-895
Number of pages	11
ISBN (Electronic)	9781948087278
State	Published - 2018
Externally published	Yes
Event	2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018

Publication series

Name	NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume	1

Conference

Conference	2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
Country/Territory	United States
City	New Orleans
Period	1/06/18 → 6/06/18

Bibliographical note

Funding Information:
This work was supported in part by grants from the MAGNET program of the Israeli Office of the Chief Scientist (OCS); the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1); the Israel Science Foundation (grant No. 1157/16); the US NSF (IIS1252835,IIS-1562364); and an Allen Distinguished Investigator Award.

Publisher Copyright:
© 2018 The Association for Computational Linguistics.

Cite this

@inproceedings{0806a5ec470c43e689c0f3e4be6a8bf7,

title = "Supervised open information extraction",

abstract = "We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available, 1 outperforms the state-of-The-Art in Open IE on benchmark datasets.",

author = "Gabriel Stanovsky and Julian Michael and Luke Zettlemoyer and Ido Dagan",

note = "Funding Information: This work was supported in part by grants from the MAGNET program of the Israeli Office of the Chief Scientist (OCS); the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1); the Israel Science Foundation (grant No. 1157/16); the US NSF (IIS1252835,IIS-1562364); and an Allen Distinguished Investigator Award. Publisher Copyright: {\textcopyright} 2018 The Association for Computational Linguistics.; 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 01-06-2018 Through 06-06-2018",

year = "2018",

language = "American English",

series = "NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",

publisher = "Association for Computational Linguistics (ACL)",

pages = "885--895",

booktitle = "Long Papers",

address = "United States",

}

Stanovsky, G, Michael, J, Zettlemoyer, L & Dagan, I 2018, Supervised open information extraction. in Long Papers. NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, vol. 1, Association for Computational Linguistics (ACL), pp. 885-895, 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018, New Orleans, United States, 1/06/18.

Supervised open information extraction. / Stanovsky, Gabriel; Michael, Julian; Zettlemoyer, Luke et al.
Long Papers. Association for Computational Linguistics (ACL), 2018. p. 885-895 (NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference; Vol. 1).

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

TY - GEN

T1 - Supervised open information extraction

AU - Stanovsky, Gabriel

AU - Michael, Julian

AU - Zettlemoyer, Luke

AU - Dagan, Ido

N1 - Funding Information: This work was supported in part by grants from the MAGNET program of the Israeli Office of the Chief Scientist (OCS); the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1); the Israel Science Foundation (grant No. 1157/16); the US NSF (IIS1252835,IIS-1562364); and an Allen Distinguished Investigator Award. Publisher Copyright: © 2018 The Association for Computational Linguistics.

PY - 2018

Y1 - 2018

N2 - We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available, 1 outperforms the state-of-The-Art in Open IE on benchmark datasets.

AB - We present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate. We also develop a bi-LSTM transducer, extending recent deep Semantic Role Labeling models to extract Open IE tuples and provide confidence scores for tuning their precision-recall tradeoff. Furthermore, we show that the recently released Question-Answer Meaning Representation dataset can be automatically converted into an Open IE corpus which significantly increases the amount of available training data. Our supervised model, made publicly available, 1 outperforms the state-of-The-Art in Open IE on benchmark datasets.

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M3 - Conference contribution

AN - SCOPUS:85064982719

T3 - NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference

SP - 885

EP - 895

BT - Long Papers

PB - Association for Computational Linguistics (ACL)

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Y2 - 1 June 2018 through 6 June 2018

ER -

Supervised open information extraction

Abstract

Publication series

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Bibliographical note

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