Joint Modeling of Opinion Expression Extraction and Attribute Classification

Bishan Yang, Claire Cardie


Abstract
In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Extensive experiments on a standard dataset demonstrate that our approaches provide substantial improvements over previously published results. By analyzing the results, we gain some insight into the advantages of different joint models.
Anthology ID:
Q14-1039
Volume:
Transactions of the Association for Computational Linguistics, Volume 2
Month:
Year:
2014
Address:
Cambridge, MA
Editors:
Dekang Lin, Michael Collins, Lillian Lee
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
505–516
Language:
URL:
https://aclanthology.org/Q14-1039
DOI:
10.1162/tacl_a_00199
Bibkey:
Cite (ACL):
Bishan Yang and Claire Cardie. 2014. Joint Modeling of Opinion Expression Extraction and Attribute Classification. Transactions of the Association for Computational Linguistics, 2:505–516.
Cite (Informal):
Joint Modeling of Opinion Expression Extraction and Attribute Classification (Yang & Cardie, TACL 2014)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/Q14-1039.pdf
Data
MPQA Opinion Corpus