Abstract
This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new state-of-the-art results on the Czech ABSA.- Anthology ID:
- 2023.ranlp-1.96
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 888–897
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.96
- DOI:
- Cite (ACL):
- Pavel Přibáň and Ondrej Prazak. 2023. Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 888–897, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model (Přibáň & Prazak, RANLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2023.ranlp-1.96.pdf