Multichannel LSTM-CNN for Telugu Text Classification

Sunil Gundapu, Radhika Mamidi


Abstract
With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing. Thematic keywords give a compressed representation of the text. Usually, Domain Identification plays a significant role in Machine Translation, Text Summarization, Question Answering, Information Extraction, and Sentiment Analysis. In this paper, we proposed the Multichannel LSTM-CNN methodology for Technical Domain Identification for Telugu. This architecture was used and evaluated in the context of the ICON shared task “TechDOfication 2020” (task h), and our system got 69.9% of the F1 score on the test dataset and 90.01% on the validation set.
Anthology ID:
2020.icon-techdofication.3
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:
11–15
Language:
URL:
https://aclanthology.org/2020.icon-techdofication.3
DOI:
Bibkey:
Cite (ACL):
Sunil Gundapu and Radhika Mamidi. 2020. Multichannel LSTM-CNN for Telugu Text Classification. In Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task, pages 11–15, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Multichannel LSTM-CNN for Telugu Text Classification (Gundapu & Mamidi, ICON 2020)
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PDF:
https://aclanthology.org/2020.icon-techdofication.3.pdf