Abstract
Identification of argumentative components is an important stage of argument mining. Lexicon information is reported as one of the most frequently used features in the argument mining research. In this paper, we propose a methodology to integrate lexicon information into a neural network model by attention mechanism. We conduct experiments on the UKP dataset, which is collected from heterogeneous sources and contains several text types, e.g., microblog, Wikipedia, and news. We explore lexicons from various application scenarios such as sentiment analysis and emotion detection. We also compare the experimental results of leveraging different lexicons.- Anthology ID:
- W19-4508
- Volume:
- Proceedings of the 6th Workshop on Argument Mining
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Benno Stein, Henning Wachsmuth
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 67–73
- Language:
- URL:
- https://aclanthology.org/W19-4508
- DOI:
- 10.18653/v1/W19-4508
- Cite (ACL):
- Jian-Fu Lin, Kuo Yu Huang, Hen-Hsen Huang, and Hsin-Hsi Chen. 2019. Lexicon Guided Attentive Neural Network Model for Argument Mining. In Proceedings of the 6th Workshop on Argument Mining, pages 67–73, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Lexicon Guided Attentive Neural Network Model for Argument Mining (Lin et al., ArgMining 2019)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-4508.pdf