@inproceedings{mukherjee-etal-2019-robust,
title = "Robust Deep Learning Based Sentiment Classification of Code-Mixed Text",
author = "Mukherjee, Siddhartha and
Prasan, Vinuthkumar and
Nediyanchath, Anish and
Shah, Manan and
Kumar, Nikhil",
editor = "Sharma, Dipti Misra and
Bhattacharya, Pushpak",
booktitle = "Proceedings of the 16th International Conference on Natural Language Processing",
month = dec,
year = "2019",
address = "International Institute of Information Technology, Hyderabad, India",
publisher = "NLP Association of India",
url = "https://aclanthology.org/2019.icon-1.14",
pages = "124--129",
abstract = "India is one of unique countries in the world that has the legacy of diversity of languages. Most of these languages are influenced by English. This causes a large presence of code-mixed text in Social Media. Enormous presence of this code-mixed text provides an important research area for Natural Language Processing (NLP). This paper proposes a novel Attention based deep learning technique for Sentiment Classification on Code-Mixed Text (ACCMT) of Hindi-English. The proposed architecture uses fusion of character and word features. Non availability of suitable Word Embedding to represent these Code-Mixed texts is another important hurdle for this league of NLP tasks. This paper also proposes a novel technique for preparing Word Embedding of Code-Mixed text. This embedding is prepared with two separately trained word-embedding on Romanized Hindi and English respectively. This embedding is further used in the proposed deep learning based architecture for robust classification. The Proposed technique achieves 71.97{\%} accuracy, which exceeds the baseline accuracy.",
}
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<abstract>India is one of unique countries in the world that has the legacy of diversity of languages. Most of these languages are influenced by English. This causes a large presence of code-mixed text in Social Media. Enormous presence of this code-mixed text provides an important research area for Natural Language Processing (NLP). This paper proposes a novel Attention based deep learning technique for Sentiment Classification on Code-Mixed Text (ACCMT) of Hindi-English. The proposed architecture uses fusion of character and word features. Non availability of suitable Word Embedding to represent these Code-Mixed texts is another important hurdle for this league of NLP tasks. This paper also proposes a novel technique for preparing Word Embedding of Code-Mixed text. This embedding is prepared with two separately trained word-embedding on Romanized Hindi and English respectively. This embedding is further used in the proposed deep learning based architecture for robust classification. The Proposed technique achieves 71.97% accuracy, which exceeds the baseline accuracy.</abstract>
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%0 Conference Proceedings
%T Robust Deep Learning Based Sentiment Classification of Code-Mixed Text
%A Mukherjee, Siddhartha
%A Prasan, Vinuthkumar
%A Nediyanchath, Anish
%A Shah, Manan
%A Kumar, Nikhil
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F mukherjee-etal-2019-robust
%X India is one of unique countries in the world that has the legacy of diversity of languages. Most of these languages are influenced by English. This causes a large presence of code-mixed text in Social Media. Enormous presence of this code-mixed text provides an important research area for Natural Language Processing (NLP). This paper proposes a novel Attention based deep learning technique for Sentiment Classification on Code-Mixed Text (ACCMT) of Hindi-English. The proposed architecture uses fusion of character and word features. Non availability of suitable Word Embedding to represent these Code-Mixed texts is another important hurdle for this league of NLP tasks. This paper also proposes a novel technique for preparing Word Embedding of Code-Mixed text. This embedding is prepared with two separately trained word-embedding on Romanized Hindi and English respectively. This embedding is further used in the proposed deep learning based architecture for robust classification. The Proposed technique achieves 71.97% accuracy, which exceeds the baseline accuracy.
%U https://aclanthology.org/2019.icon-1.14
%P 124-129
Markdown (Informal)
[Robust Deep Learning Based Sentiment Classification of Code-Mixed Text](https://aclanthology.org/2019.icon-1.14) (Mukherjee et al., ICON 2019)
ACL
- Siddhartha Mukherjee, Vinuthkumar Prasan, Anish Nediyanchath, Manan Shah, and Nikhil Kumar. 2019. Robust Deep Learning Based Sentiment Classification of Code-Mixed Text. In Proceedings of the 16th International Conference on Natural Language Processing, pages 124–129, International Institute of Information Technology, Hyderabad, India. NLP Association of India.