Direct parsing to sentiment graphs

David Samuel, Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, Erik Velldal


Abstract
This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release the source code, models and predictions.
Anthology ID:
2022.acl-short.51
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
470–478
Language:
URL:
https://aclanthology.org/2022.acl-short.51
DOI:
10.18653/v1/2022.acl-short.51
Bibkey:
Cite (ACL):
David Samuel, Jeremy Barnes, Robin Kurtz, Stephan Oepen, Lilja Øvrelid, and Erik Velldal. 2022. Direct parsing to sentiment graphs. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 470–478, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Direct parsing to sentiment graphs (Samuel et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-short.51.pdf
Software:
 2022.acl-short.51.software.zip
Code
 jerbarnes/direct_parsing_to_sent_graph
Data
MPQA Opinion Corpus