Abstract
For a range of Natural Language Processing (NLP) applications, including Sentiment Analysis, Sarcasm Detection, Information Retrieval, Question Answering, and Named Entity Identification, text derived from multiple users’ posts and what they comment on social media constitute significant information (IR). All such applications require part-of-speech (POS) tagging to add tag information to the raw text. Code-mixing, a social media user’s natural desire to submit content in multiple languages, presents a difficulty to POS tagging. In addition, sophisticated and freestyle writing increases the intricacy of the issue. For POS tagging of Code-Mixed Indian social media text, a supervised algorithm using Hidden Markov Model (HMM) with the Viterbi algorithm has been developed to address the problem. The suggested system has been trained and tested using publicly accessible social media text in Indian languages (ILs), particularly Bengali, Telugu, English, and Hindi. On the basis of the F-measure, the accuracy of the system-annotated tags have been assessed.
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Acknowledgement
The work presented here is a part of experiments being conducted under the Research Project Grant Ref. No. N-21/17/2020-NeGD supported by MeitY Quantum Computing Applications Lab (QCAL) and Amazon-braket. We also extend our gratitude to the Department of CSE, NIT Silchar, and the Center for Natural Language Processing for their support.
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Basisth, N.J., Sachan, T., Kumari, N., Pandey, S., Pakray, P. (2024). An Automatic POS Tagger System for Code Mixed Indian Social Media Text. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1956. Springer, Cham. https://doi.org/10.1007/978-3-031-48879-5_21
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