A Hybrid Approach for the Acquisition of Information Extraction Patterns

Mihai Surdeanu; Jordi Turmo; Alicia Ageno

A Hybrid Approach for the Acquisition of Information Extraction Patterns

Mihai Surdeanu, Jordi Turmo, Alicia Ageno

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper we present a hybrid approach for the acquisition of syntacticosemantic patterns from raw text. Our approach co-trains a decision list learner whose feature space covers the set of all syntactico-semantic patterns with an Expectation Maximization clustering algorithm that uses the text words as attributes. We show that the combination of the two methods always outperforms the decision list learner alone. Furthermore, using a modular architecture we investigate several algorithms for pattern ranking, the most important component of the decision list learner.

Original language	English (US)
Pages	48-55
Number of pages	8
State	Published - 2006
Externally published	Yes
Event	2006 Workshop on Adaptive Text Extraction and Mining, ATEM 2006 - Trento, Italy Duration: Apr 4 2006 → …

Conference

Conference	2006 Workshop on Adaptive Text Extraction and Mining, ATEM 2006
Country/Territory	Italy
City	Trento
Period	4/4/06 → …

ASJC Scopus subject areas

Language and Linguistics
Linguistics and Language

Cite this

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title = "A Hybrid Approach for the Acquisition of Information Extraction Patterns",

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author = "Mihai Surdeanu and Jordi Turmo and Alicia Ageno",

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AU - Turmo, Jordi

AU - Ageno, Alicia

N1 - Publisher Copyright: © EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Workshop on Adaptive Text Extraction and Mining, ATEM 2006. All rights reserved.

PY - 2006

Y1 - 2006

N2 - In this paper we present a hybrid approach for the acquisition of syntacticosemantic patterns from raw text. Our approach co-trains a decision list learner whose feature space covers the set of all syntactico-semantic patterns with an Expectation Maximization clustering algorithm that uses the text words as attributes. We show that the combination of the two methods always outperforms the decision list learner alone. Furthermore, using a modular architecture we investigate several algorithms for pattern ranking, the most important component of the decision list learner.

AB - In this paper we present a hybrid approach for the acquisition of syntacticosemantic patterns from raw text. Our approach co-trains a decision list learner whose feature space covers the set of all syntactico-semantic patterns with an Expectation Maximization clustering algorithm that uses the text words as attributes. We show that the combination of the two methods always outperforms the decision list learner alone. Furthermore, using a modular architecture we investigate several algorithms for pattern ranking, the most important component of the decision list learner.

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T2 - 2006 Workshop on Adaptive Text Extraction and Mining, ATEM 2006

Y2 - 4 April 2006

ER -

A Hybrid Approach for the Acquisition of Information Extraction Patterns

Abstract

Conference

ASJC Scopus subject areas

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