Article

A probabilistic model of redundancy in information extraction

Authors:

Doug Downey,

Oren Etzioni,

Stephen SoderlandAuthors Info & Claims

IJCAI'05: Proceedings of the 19th international joint conference on Artificial intelligence

Pages 1034 - 1041

Published: 30 July 2005 Publication History

Abstract

Unsupervised Information Extraction (UIE) is the task of extracting knowledge from text without using hand-tagged training examples. A fundamental problem for both UIE and supervised IE is assessing the probability that extracted information is correct. In massive corpora such as the Web, the same extraction is found repeatedly in different documents. How does this redundancy impact the probability of correctness?

This paper introduces a combinatorial "balls-andurns" model that computes the impact of sample size, redundancy, and corroboration from multiple distinct extraction rules on the probability that an extraction is correct. We describe methods for estimating the model's parameters in practice and demonstrate experimentally that for UIE the model's log likelihoods are 15 times better, on average, than those obtained by Pointwise Mutual Information (PMI) and the noisy-or model used in previous work. For supervised IE, the model's performance is comparable to that of Support Vector Machines, and Logistic Regression.

References

[1]

{Agichtein and Gravano, 2000} E. Agichtein and L. Gravano. Snowball: Extracting relations from large plaintext collections. In Proc. of the 5th ACM Intl. Conf. on Digital Libraries, 2000.

Digital Library

Google Scholar

[2]

{Chang and Lin, 2001} C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001.

Google Scholar

[3]

{Culotta and McCallum, 2004} A. Culotta and A. McCallum. Confidence estimation for information extraction. In HLT-NAACL, 2004.

Digital Library

Google Scholar

[4]

{Etzioni et al., 2004} O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates. Web-scale information extraction in system x: (preliminary results). In WWW, 2004.

Digital Library

Google Scholar

[5]

{Etzioni et al., 2005} O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates. Unsupervised named-entity extraction from the web: An experimental study. In To appear in AIJ, 2005.

Google Scholar

[6]

{Gale and Sampson, 1995} W. A. Gale and G. Sampson. Good-turing frequency estimation without tears. Journal of Quantitative Linguistics, 2(3):217-237, 1995.

Crossref

Google Scholar

[7]

{Lin et al., 2003} W. Lin, R. Yangarber, and R. Grishman. Bootstrapped learning of semantic classes. In ICML Workshop on The Continuum from Labeled to Unlabeled Data, 2003.

Google Scholar

[8]

{Magnini et al., 2002} B. Magnini, M. Negri, R. Prevete, and H. Tanev. Is it the right answer? exploiting web redundancy for answer validation. In ACL, 2002.

Digital Library

Google Scholar

[9]

{Milch et al., 2004} B. Milch, B. Marthi, and S. Russell. BLOG: Relational modeling with unknown objects. In ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields, 2004.

Google Scholar

[10]

{Riloff and Jones, 1999} E. Riloff and R. Jones. Learning dictionaries for information extraction by multi-level bootstrapping. In AAAI/IAAI, 1999.

Digital Library

Google Scholar

[11]

{Skounakis and Craven, 2003} M. Skounakis and M. Craven. Evidence combination in biomedical natural-language processing. In BIOKDD, 2003.

Google Scholar

[12]

{Turney, 2001} P. D. Turney. Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. Lecture Notes in Computer Science, 2167:491-502, 2001.

Digital Library

Google Scholar

Cited By

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Yan JWang CCheng WGao MZhou A(2018)A retrospective of knowledge graphsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5228-912:1(55-74)Online publication date: 1-Feb-2018
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Chodpathumwan YVakilian ATermehchy ANayyeri A(2018)Cost-effective conceptual design using taxonomiesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0501-127:3(369-394)Online publication date: 1-Jun-2018
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IJCAI'05: Proceedings of the 19th international joint conference on Artificial intelligence

July 2005

1775 pages

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Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

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Published: 30 July 2005

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Yan JWang CCheng WGao MZhou A(2018)A retrospective of knowledge graphsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5228-912:1(55-74)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11704-016-5228-9
Chodpathumwan YVakilian ATermehchy ANayyeri A(2018)Cost-effective conceptual design using taxonomiesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0501-127:3(369-394)Online publication date: 1-Jun-2018
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Konovalov AStrauss BRitter AO'Connor BBarrett RCummings RAgichtein EGabrilovich E(2017)Learning to Extract Events from Knowledge Base RevisionsProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052646(1007-1014)Online publication date: 3-Apr-2017
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Balaji JRanjani PGeetha T(2016)Unsupervised learning of semantic relations of a morphologically rich languageInternational Journal of Information and Communication Technology10.5555/2961060.29610648:4(344-356)Online publication date: 1-Jan-2016
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Termehchy AVakilian AChodpathumwan YWinslett M(2015)Cost-Effective Conceptual Design for Information ExtractionACM Transactions on Database Systems10.1145/271632140:2(1-39)Online publication date: 30-Jun-2015
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Analysis of a probabilistic model of redundancy in unsupervised information extraction

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