@inproceedings{ghosh-etal-2020-technical,
    title = "Technical Domain Identification using word2vec and {B}i{LSTM}",
    author = "Ghosh, Koyel  and
      Senapati, Dr. Apurbalal  and
      Maity, Dr. Ranjan",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/2020.icon-techdofication.5/",
    pages = "21--26",
    abstract = "Coarse-grained and Fine-grained classification tasks are mostly based on sentiment or basic emotion analysis. Now, switching from emotion and sentiment analysis to another domain, in this paper, we are going to work on technical domain identification. The task is to identify the technical domain of a given English text. In the case of Coarse-grained domain classification, such a piece of text provides information about specific Coarse-grained technical domains like Computer Science, Physics, Math, etc, and in Fine-grained domain classification, Fine-grained subdomains for Computer science domain, it can be like Artificial Intelligence, Algorithm, Computer Architecture, Computer Networks, Database Management system, etc. To do the task, Word2Vec skip-gram model is used for word embedding, later, applied the Bidirectional Long Short Term memory (BiLSTM) model to classify Coarse-grained domains and Fine-grained sub-domains. To evaluate the performance of the approached model accuracy, precision, recall, and F1-score have been applied."
}