Active learning for relation type extension with local and global data views

Ang Sun; Ralph Grishman

doi:10.1145/2396761.2398409

Active learning for relation type extension with local and global data views

Ang Sun, Ralph Grishman

Computer Science

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

Abstract

Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.

Original language	English (US)
Title of host publication	CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages	1105-1112
Number of pages	8
DOIs	https://doi.org/10.1145/2396761.2398409
State	Published - 2012
Event	21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: Oct 29 2012 → Nov 2 2012

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/Territory	United States
City	Maui, HI
Period	10/29/12 → 11/2/12

Keywords

co-testing
co-training
distributional similarity
infactive learning
information extraction
inrelation extraction

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2396761.2398409

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Active learning for relation type extension with local and global data views. / Sun, Ang; Grishman, Ralph.
CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 1105-1112 (ACM International Conference Proceeding Series).

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

Sun, A & Grishman, R 2012, Active learning for relation type extension with local and global data views. in CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM International Conference Proceeding Series, pp. 1105-1112, 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 10/29/12. https://doi.org/10.1145/2396761.2398409

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Active learning for relation type extension with local and global data views

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

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