Automatic committed belief tagging

Vinodkumar Prabhakaran; Owen Rambow; Mona Diab

Automatic committed belief tagging

Vinodkumar Prabhakaran
, Owen Rambow
, Mona Diab

Linguistics

Columbia University

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

39 Scopus citations

Abstract

We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an Fmeasure of 64%, a relative reduction in Fmeasure error of 21% over not using syntactic features.

Original language	English
Pages	1014-1022
Number of pages	9
State	Published - 2010
Event	23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: Aug 23 2010 → Aug 27 2010

Conference

Conference	23rd International Conference on Computational Linguistics, Coling 2010
Country/Territory	China
City	Beijing
Period	08/23/10 → 08/27/10

Cite this

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title = "Automatic committed belief tagging",

abstract = "We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an Fmeasure of 64\%, a relative reduction in Fmeasure error of 21\% over not using syntactic features.",

author = "Vinodkumar Prabhakaran and Owen Rambow and Mona Diab",

year = "2010",

language = "English",

pages = "1014--1022",

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

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TY - CONF

T1 - Automatic committed belief tagging

AU - Prabhakaran, Vinodkumar

AU - Rambow, Owen

AU - Diab, Mona

PY - 2010

Y1 - 2010

N2 - We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an Fmeasure of 64%, a relative reduction in Fmeasure error of 21% over not using syntactic features.

AB - We go beyond simple propositional meaning extraction and present experiments in determining which propositions in text the author believes. We show that deep syntactic parsing helps for this task. Our best feature combination achieves an Fmeasure of 64%, a relative reduction in Fmeasure error of 21% over not using syntactic features.

UR - https://www.scopus.com/pages/publications/80053396213

M3 - Paper

AN - SCOPUS:80053396213

SP - 1014

EP - 1022

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

Y2 - 23 August 2010 through 27 August 2010

ER -

Automatic committed belief tagging

Abstract

Conference

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