Extracting Opinions, Opinion Holders, and Topics
Expressed in Online News Media Text
S. Kim, and E. Hovy. Proceedings of ACL/COLING Workshop on Sentiment and Subjectivity in Text, Sidney, AUS, (2006)
Abstract
This paper presents a method for identifying an
opinion with its holder and topic, given a sentence
from online news media texts. We introduce an approach
of exploiting the semantic structure of a sentence,
anchored to an opinion bearing verb or adjective. This
method uses semantic role labeling as an intermediate
step to label an opinion holder and topic using data
from FrameNet. We decompose our task into three phases:
identifying an opinion-bearing word, labeling semantic
roles related to the word in the sentence, and then
finding the holder and the topic of the opinion word
among the labeled semantic roles. For a broader
coverage, we also employ a clustering technique to
predict the most probable frame for a word which is not
defined in FrameNet. Our experimental results show that
our system performs significantly better than the
baseline.
%0 Conference Paper
%1 Kim06
%A Kim, Soo-Min
%A Hovy, Eduard
%B Proceedings of ACL/COLING Workshop on Sentiment and Subjectivity in Text
%C Sidney, AUS
%D 2006
%K #politics clustering extractions opinion
%T Extracting Opinions, Opinion Holders, and Topics
Expressed in Online News Media Text
%U http://www.isi.edu/~skim/Download/Papers/2006/Topic_and_Holder_ACL06WS.pdf
%X This paper presents a method for identifying an
opinion with its holder and topic, given a sentence
from online news media texts. We introduce an approach
of exploiting the semantic structure of a sentence,
anchored to an opinion bearing verb or adjective. This
method uses semantic role labeling as an intermediate
step to label an opinion holder and topic using data
from FrameNet. We decompose our task into three phases:
identifying an opinion-bearing word, labeling semantic
roles related to the word in the sentence, and then
finding the holder and the topic of the opinion word
among the labeled semantic roles. For a broader
coverage, we also employ a clustering technique to
predict the most probable frame for a word which is not
defined in FrameNet. Our experimental results show that
our system performs significantly better than the
baseline.
@inproceedings{Kim06,
abstract = {This paper presents a method for identifying an
opinion with its holder and topic, given a sentence
from online news media texts. We introduce an approach
of exploiting the semantic structure of a sentence,
anchored to an opinion bearing verb or adjective. This
method uses semantic role labeling as an intermediate
step to label an opinion holder and topic using data
from FrameNet. We decompose our task into three phases:
identifying an opinion-bearing word, labeling semantic
roles related to the word in the sentence, and then
finding the holder and the topic of the opinion word
among the labeled semantic roles. For a broader
coverage, we also employ a clustering technique to
predict the most probable frame for a word which is not
defined in FrameNet. Our experimental results show that
our system performs significantly better than the
baseline.},
added-at = {2014-08-08T14:36:38.000+0200},
address = {Sidney, {AUS}},
author = {Kim, Soo-Min and Hovy, Eduard},
biburl = {https://www.bibsonomy.org/bibtex/261827fbc7fdb80d5c9166b267fae8594/dallmann},
booktitle = {Proceedings of ACL/COLING Workshop on Sentiment and Subjectivity in Text},
interhash = {eccee87bd561f895bdc10068b26dc1e6},
intrahash = {61827fbc7fdb80d5c9166b267fae8594},
keywords = {#politics clustering extractions opinion},
pdf = {Kim06.pdf},
timestamp = {2014-08-08T14:36:38.000+0200},
title = {Extracting Opinions, Opinion Holders, and Topics
Expressed in Online News Media Text},
url = {http://www.isi.edu/~skim/Download/Papers/2006/Topic_and_Holder_ACL06WS.pdf},
year = 2006
}