Representing text for joint embedding of text and knowledge bases

Kristina Toutanova; Danqi Chen; Patrick Pantel; Hoifung Poon; Pallavi Choudhury; Michael Gamon

doi:10.18653/v1/d15-1174

Representing text for joint embedding of text and knowledge bases

Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, Michael Gamon

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

638 Scopus citations

Abstract

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

Original language	English (US)
Title of host publication	Conference Proceedings - EMNLP 2015
Subtitle of host publication	Conference on Empirical Methods in Natural Language Processing
Publisher	Association for Computational Linguistics (ACL)
Pages	1499-1509
Number of pages	11
ISBN (Electronic)	9781941643327
DOIs	https://doi.org/10.18653/v1/d15-1174
State	Published - 2015
Externally published	Yes
Event	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal Duration: Sep 17 2015 → Sep 21 2015

Publication series

Name	Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Other

Other	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015
Country/Territory	Portugal
City	Lisbon
Period	9/17/15 → 9/21/15

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Access to Document

10.18653/v1/d15-1174

Cite this

Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., & Gamon, M. (2015). Representing text for joint embedding of text and knowledge bases. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 1499-1509). (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1174

Toutanova, Kristina ; Chen, Danqi ; Pantel, Patrick et al. / Representing text for joint embedding of text and knowledge bases. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2015. pp. 1499-1509 (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing).

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title = "Representing text for joint embedding of text and knowledge bases",

abstract = "Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.",

author = "Kristina Toutanova and Danqi Chen and Patrick Pantel and Hoifung Poon and Pallavi Choudhury and Michael Gamon",

note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 ; Conference date: 17-09-2015 Through 21-09-2015",

year = "2015",

doi = "10.18653/v1/d15-1174",

language = "English (US)",

series = "Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing",

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Toutanova, K, Chen, D, Pantel, P, Poon, H, Choudhury, P & Gamon, M 2015, Representing text for joint embedding of text and knowledge bases. in Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (ACL), pp. 1499-1509, Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 9/17/15. https://doi.org/10.18653/v1/d15-1174

Representing text for joint embedding of text and knowledge bases. / Toutanova, Kristina; Chen, Danqi; Pantel, Patrick et al.
Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2015. p. 1499-1509 (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing).

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

TY - GEN

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AU - Chen, Danqi

AU - Pantel, Patrick

AU - Poon, Hoifung

AU - Choudhury, Pallavi

AU - Gamon, Michael

PY - 2015

Y1 - 2015

N2 - Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

AB - Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

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M3 - Conference contribution

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T3 - Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

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BT - Conference Proceedings - EMNLP 2015

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Toutanova K, Chen D, Pantel P, Poon H, Choudhury P, Gamon M. Representing text for joint embedding of text and knowledge bases. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL). 2015. p. 1499-1509. (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). doi: 10.18653/v1/d15-1174

Representing text for joint embedding of text and knowledge bases

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