An Uncertainty-Aware Encoder for Aspect Detection
Thi-Nhung Nguyen, Kiem-Hieu Nguyen, Young-In Song, Tuan-Dung Cao
Abstract
Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.- Anthology ID:
- 2021.findings-emnlp.69
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 797–806
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.69
- DOI:
- 10.18653/v1/2021.findings-emnlp.69
- Cite (ACL):
- Thi-Nhung Nguyen, Kiem-Hieu Nguyen, Young-In Song, and Tuan-Dung Cao. 2021. An Uncertainty-Aware Encoder for Aspect Detection. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 797–806, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- An Uncertainty-Aware Encoder for Aspect Detection (Nguyen et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.findings-emnlp.69.pdf
- Code
- nhungnt7/UCE
- Data
- OpoSum