@inproceedings{kuila-etal-2020-graph,
title = "A Graph Convolution Network-based System for Technical Domain Identification",
author = "Kuila, Alapan and
Das, Ayan and
Sarkar, Sudeshna",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task",
month = dec,
year = "2020",
address = "Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-techdofication.2",
pages = "6--10",
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).",
}
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<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).</abstract>
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%0 Conference Proceedings
%T A Graph Convolution Network-based System for Technical Domain Identification
%A Kuila, Alapan
%A Das, Ayan
%A Sarkar, Sudeshna
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task
%D 2020
%8 dec
%I NLP Association of India (NLPAI)
%C Patna, India
%F kuila-etal-2020-graph
%X 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).
%U https://aclanthology.org/2020.icon-techdofication.2
%P 6-10
Markdown (Informal)
[A Graph Convolution Network-based System for Technical Domain Identification](https://aclanthology.org/2020.icon-techdofication.2) (Kuila et al., ICON 2020)
ACL