Abstract
Language Identification in code-mixed social media text contest aimed at Multilingual Meta Embeddings (MME), a productive method to learn multilingual representations for Language Identification. Language mixing occurs at a sentence boundary, within a sentence, or a word in code-mixing. This paper proffers an MME-driven language identification mechanism for code-mixed text. This study zeroed in on the comparison of different classifiers on Hindi-English code-mixed text data obtained from LinCE Benchmark corpus. LinCE is a centralized benchmark for linguistic code-switching evaluation that integrates ten corpora from four different code-switched language pairings with four tasks. Each instance in the dataset was a code-mixed sentence, and each token in the sentence was associated with a language label. Then we experimented with using different classifiers such as convolutional neural network, Gated Recurrent Unit, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit and we observed BiLstm outperformed well. A multilingual meta embedding technique was empirically evaluated for language identification.
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References
Thara S, Poornachandran P (2018) Code-mixing: a brief survey. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 2382–2388
Sravani L, Reddy AS, Thara S (2018) A comparison study of word embedding for detecting named entities of code-mixed data in Indian language. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 2375–2381
Aguilar G, Kar S, Solorio T (2020) LinCE: a centralized benchmark for linguistic code-switching evaluation. arXiv:2005.04322
Chaitanya I et al (2018) Word level language identification in code-mixed data using word embedding methods for indian languages. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE
Veena PVM, Kumar A, Soman KP (2017) An effective way of word-level language identification for code-mixed facebook comments using word-embedding via character-embedding. In: 2017 International conference on advances in computing, communications and informatics (ICACCI). IEEE
Winata GI, Lin Z, Fung P (2019) Learning multilingual meta-embeddings for code-switching named entity recognition. In: Proceedings of the 4th workshop on representation learning for NLP (RepL4NLP-2019)
Bollegala D, Bao C (2018) Learning word meta-embeddings by autoencoding. In: Proceedings of the 27th international conference on computational linguistics
Thara S, Poornachandran P (2021) Transformer based language identification for malayalam-english code-mixed text. IEEE Access 11(9):118837–118850
Grave E, Bojanowski P, Gupta P, Joulin A, Mikolov T (2018) Learning word vectors for 157 languages. In: Proceedings of the international conference on language resources and evaluation (LREC 2018)
Sreelakshmi K, Premjith B, Amrita_CEN_NLP@ DravidianLangTech-EACL2021 KS (2021) deep learning-based offensive language identification in Malayalam, Tamil and Kannada. In: Proceedings of the first workshop on speech and language technologies for dravidian languages, pp 249–254
Ruder S (2017) An overview of multi-task learning in deep neural networks
Singh K, Sen I, Kumaraguru P (2018) Lan-guage identification and named entity recognition in hinglish code mixedtweets. In: Proceedings of ACL 2018, student research workshop, pp 52–58. https://doi.org/10.18653/v1/P18-3008
Sharma A, Gupta S, Motlani R, Bansal P, Shrivastava M, Mamidi R, Sharma DM (2016) Shallow parsing pipeline—Hindi-English code-mixed social media text. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. https://doi.org/10.18653/v1/N16-1159
Khapra MM, Ramanathan A, Kunchukuttan A, Visweswariah K, Bhattacharyya P, When transliteration met crowdsourcing : an empirical study of transliteration via crowdsourcing using efficient, non-redundant and fair quality control. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14)
Gupta DK, Kumar S, Ekbal A (2014) Machine learning approach for language identification and transliteration. In: Proceedings of the forum for information retrieval evaluation
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Teja, T.R., Shilpa, S., Joseph, N. (2023). Meta Embeddings for LinCE Dataset. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_26
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DOI: https://doi.org/10.1007/978-981-19-7874-6_26
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