Learning Subjective Nouns using Extraction Pattern Bootstrapping

Ellen Riloff; Janyce Wiebe; Theresa Wilson

Learning Subjective Nouns using Extraction Pattern Bootstrapping

Ellen Riloff
, Janyce Wiebe
, Theresa Wilson

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

Abstract

We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.

Original language	English (US)
Pages	25-32
Number of pages	8
State	Published - 2003
Externally published	Yes
Event	7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003 - Edmonton, Canada Duration: May 31 2003 → Jun 1 2003

Conference

Conference	7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003
Country/Territory	Canada
City	Edmonton
Period	5/31/03 → 6/1/03

ASJC Scopus subject areas

Management Science and Operations Research
Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Modeling and Simulation

Cite this

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title = "Learning Subjective Nouns using Extraction Pattern Bootstrapping",

abstract = "We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77\% recall with 81\% precision.",

author = "Ellen Riloff and Janyce Wiebe and Theresa Wilson",

note = "Publisher Copyright: {\textcopyright} 2003 Proceedings of the 7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003. All rights reserved.; 7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003 ; Conference date: 31-05-2003 Through 01-06-2003",

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pages = "25--32",

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TY - CONF

T1 - Learning Subjective Nouns using Extraction Pattern Bootstrapping

AU - Riloff, Ellen

AU - Wiebe, Janyce

AU - Wilson, Theresa

PY - 2003

Y1 - 2003

N2 - We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.

AB - We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.

UR - https://www.scopus.com/pages/publications/85086069941

UR - https://www.scopus.com/pages/publications/85086069941#tab=citedBy

M3 - Paper

AN - SCOPUS:85086069941

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T2 - 7th Conference on Natural Language Learning, CoNLL 2003 at HLT-NAACL 2003

Y2 - 31 May 2003 through 1 June 2003

ER -

Learning Subjective Nouns using Extraction Pattern Bootstrapping

Abstract

Conference

ASJC Scopus subject areas

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