@inproceedings{gundapu-mamidi-2020-multichannel,
title = "Multichannel {LSTM}-{CNN} for {T}elugu Text Classification",
author = "Gundapu, Sunil and
Mamidi, Radhika",
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.3",
pages = "11--15",
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.",
}
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%0 Conference Proceedings
%T Multichannel LSTM-CNN for Telugu Text Classification
%A Gundapu, Sunil
%A Mamidi, Radhika
%Y Sharma, Dipti Misra
%Y Ekbal, Asif
%Y Arora, Karunesh
%Y Naskar, Sudip Kumar
%Y Ganguly, Dipankar
%Y L, Sobha
%Y Mamidi, Radhika
%Y Arora, Sunita
%Y Mishra, Pruthwik
%Y Mujadia, Vandan
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON): TechDOfication 2020 Shared Task
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Patna, India
%F gundapu-mamidi-2020-multichannel
%X 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.
%U https://aclanthology.org/2020.icon-techdofication.3
%P 11-15
Markdown (Informal)
[Multichannel LSTM-CNN for Telugu Text Classification](https://aclanthology.org/2020.icon-techdofication.3) (Gundapu & Mamidi, ICON 2020)
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).