# Multichannel LSTM-CNN for Telugu Technical Domain Identification

arXiv: https://arxiv.org/abs/2102.12179 · HTML: https://mlsanity.com/paper/2102.12179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Multichannel CNN with Attention for Text Classification](https://mlsanity.com/paper/2006.16174/) (2020)

## Abstract (arXiv)

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.