Learning Extraction Patterns for Subjective Expressions

Ellen Riloff; Janyce Wiebe

doi:10.3115/1119355.1119369

Learning Extraction Patterns for Subjective Expressions

Ellen Riloff
, Janyce Wiebe

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

734 Scopus citations

Abstract

This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.

Original language	English (US)
Pages	105-112
Number of pages	8
DOIs	https://doi.org/10.3115/1119355.1119369
State	Published - 2003
Externally published	Yes
Event	8th Conference on Empirical Methods in Natural Language Processing, EMNLP 2003 - Sapporo, Japan Duration: Jul 11 2003 → Jul 12 2003

Conference

Conference	8th Conference on Empirical Methods in Natural Language Processing, EMNLP 2003
Country/Territory	Japan
City	Sapporo
Period	7/11/03 → 7/12/03

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

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10.3115/1119355.1119369

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title = "Learning Extraction Patterns for Subjective Expressions",

abstract = "This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.",

author = "Ellen Riloff and Janyce Wiebe",

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AB - This paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. The learned patterns are then used to identify more subjective sentences. The bootstrapping process learns many subjective patterns and increases recall while maintaining high precision.

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DO - 10.3115/1119355.1119369

M3 - Paper

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T2 - 8th Conference on Empirical Methods in Natural Language Processing, EMNLP 2003

Y2 - 11 July 2003 through 12 July 2003

ER -

Learning Extraction Patterns for Subjective Expressions

Abstract

Conference

ASJC Scopus subject areas

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