@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",
    editor = "Sharma, Dipti Misra  and
      Ekbal, Asif  and
      Arora, Karunesh  and
      Naskar, Sudip Kumar  and
      Ganguly, Dipankar  and
      L, Sobha  and
      Mamidi, Radhika  and
      Arora, Sunita  and
      Mishra, Pruthwik  and
      Mujadia, Vandan",
    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).",
}