Abstract
This paper presents the IITKGP contribution at the Technical DOmain Identification (TechDOfication) shared task at ICON 2020. In the preprocessing stage, we applied part-of-speech (PoS) taggers and dependency parsers to tag the data. We trained a graph convolution neural network (GCNN) based system that uses the tokens along with their PoS and dependency relations as features to identify the domain of a given document. We participated in the subtasks for coarse-grained domain classification in the English (Subtask 1a), Bengali (Subtask 1b) and Hindi language (Subtask 1d), and, the subtask for fine-grained domain classification task within Computer Science domain in English language (Subtask 2a).- Anthology ID:
- 2020.icon-techdofication.2
- Volume:
- Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task
- Month:
- December
- Year:
- 2020
- Address:
- Patna, India
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 6–10
- Language:
- URL:
- https://aclanthology.org/2020.icon-techdofication.2
- DOI:
- Cite (ACL):
- Alapan Kuila, Ayan Das, and Sudeshna Sarkar. 2020. A Graph Convolution Network-based System for Technical Domain Identification. In Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task, pages 6–10, Patna, India. NLP Association of India (NLPAI).
- Cite (Informal):
- A Graph Convolution Network-based System for Technical Domain Identification (Kuila et al., ICON 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.icon-techdofication.2.pdf