Md Akhtar
2022
A Novel Approach towards Cross Lingual Sentiment Analysis using Transliteration and Character Embedding
Rajarshi Roychoudhury
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Subhrajit Dey
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Md Akhtar
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Amitava Das
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Sudip Naskar
Proceedings of the 19th International Conference on Natural Language Processing (ICON)
Sentiment analysis with deep learning in resource-constrained languages is a challenging task. In this paper, we introduce a novel approach for sentiment analysis in resource-constrained scenarios using character embedding and cross-lingual sentiment analysis with transliteration. We use this method to introduce the novel task of inducing sentiment polarity of words and sentences and aspect term sentiment analysis in the no-resource scenario. We formulate this task by taking a metalingual approach whereby we transliterate data from closely related languages and transform it into a meta language. We also demonstrated the efficacy of using character-level embedding for sentence representation. We experimented with 4 Indian languages – Bengali, Hindi, Tamil, and Telugu, and obtained encouraging results. We also presented new state-of-the-art results on the Hindi sentiment analysis dataset leveraging our metalingual character embeddings.
Normalization of Spelling Variations in Code-Mixed Data
Krishna Yadav
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Md Akhtar
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Tanmoy Chakraborty
Proceedings of the 19th International Conference on Natural Language Processing (ICON)
Code-mixed text infused with low resource language has always been a challenge for natural language understanding models. A significant problem while understanding such texts is the correlation between the syntactic and semantic arrangement of words. The phonemes of each character in a word dictates the spelling representation of a term in low resource language. However, there is no universal protocol or alphabet mapping for code-mixing. In this paper, we highlight the impact of spelling variations in code-mixed data for training natural language understanding models. We emphasize the impact of using phonetics to neutralize this variation in spelling across different usage of a word with the same semantics. The proposed approach is a computationally inexpensive technique and improves the performances of state-of-the-art models for three dialog system tasks viz. intent classification, slot-filling, and response generation. We propose a data pipeline for normalizing spelling variations irrespective of language.
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Co-authors
- Rajarshi Roychoudhury 1
- Subhrajit Dey 1
- Amitava Das 1
- Sudip Kumar Naskar 1
- Krishna Yadav 1
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