Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters

Ang Sun; Ralph Grishman

Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters

Ang Sun, Ralph Grishman

Computer Science

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

Abstract

We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.

Original language	English (US)
Title of host publication	Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Pages	1194-1202
Number of pages	9
Volume	2
State	Published - 2010
Event	23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: Aug 23 2010 → Aug 27 2010

Other

Other	23rd International Conference on Computational Linguistics, Coling 2010
Country/Territory	China
City	Beijing
Period	8/23/10 → 8/27/10

ASJC Scopus subject areas

Language and Linguistics
Computational Theory and Mathematics
Linguistics and Language

Cite this

@inproceedings{9f888aa0d3974c98971be85de7166caf,

title = "Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters",

abstract = "We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.",

author = "Ang Sun and Ralph Grishman",

year = "2010",

language = "English (US)",

volume = "2",

pages = "1194--1202",

booktitle = "Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference",

note = "23rd International Conference on Computational Linguistics, Coling 2010 ; Conference date: 23-08-2010 Through 27-08-2010",

}

TY - GEN

T1 - Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters

AU - Sun, Ang

AU - Grishman, Ralph

PY - 2010

Y1 - 2010

N2 - We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.

AB - We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.

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UR - http://www.scopus.com/inward/citedby.url?scp=80053391892&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80053391892

VL - 2

SP - 1194

EP - 1202

BT - Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference

T2 - 23rd International Conference on Computational Linguistics, Coling 2010

Y2 - 23 August 2010 through 27 August 2010

ER -

Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters

Abstract

Other

ASJC Scopus subject areas

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