Technical Domain Identification using word2vec and BiLSTM

Koyel Ghosh, Dr. Apurbalal Senapati, Dr. Ranjan Maity


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.
Anthology ID:
2020.icon-techdofication.5
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:
21–26
Language:
URL:
https://aclanthology.org/2020.icon-techdofication.5
DOI:
Bibkey:
Cite (ACL):
Koyel Ghosh, Dr. Apurbalal Senapati, and Dr. Ranjan Maity. 2020. Technical Domain Identification using word2vec and BiLSTM. In Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task, pages 21–26, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Technical Domain Identification using word2vec and BiLSTM (Ghosh et al., ICON 2020)
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PDF:
https://aclanthology.org/2020.icon-techdofication.5.pdf