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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/Q14-1039.pdf
- Data
- MPQA Opinion Corpus