A Graph Convolution Network-based System for Technical Domain Identification

Alapan Kuila, Ayan Das, Sudeshna Sarkar


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:
Bibkey:
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)
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PDF:
https://aclanthology.org/2020.icon-techdofication.2.pdf