A Survey on Parts of Speech Tagging for Indian Languages

Singh, Jagjeet; Garcha, Lakhvir Singh; Singh, Satinderpal

doi:10.23956/ijarcsse/v7i4/0139

Cited by 4 publications

(1 citation statement)

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“…The research applied 6549 words for training the model and obtained an F1-score of 80%. In [21], supervised and unsupervised methods were used with multilingual parallel corpora for Tamil POS tagging. In the research, various techniques such as HMM, support vector machine (SVM) and CRF were implemented, and the SVM model was found to give the maximum accuracy (61.29%).…”

Section: Related Workmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Anbukkarasi¹,

Varadhaganapathy²

2021

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This paper addresses the problem of part of speech (POS) tagging for the Tamil language, which is low resourced and agglutinative. POS tagging is the process of assigning syntactic categories for the words in a sentence. This is the preliminary step for many of the Natural Language Processing (NLP) tasks. For this work, various sequential deep learning models such as recurrent neural network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bi-directional Long Short-Term Memory (Bi-LSTM) were used at the word level. For evaluating the model, the performance metrics such as precision, recall, F1-score and accuracy were used. Further, a tag set of 32 tags and 225 000 tagged Tamil words was utilized for training. To find the appropriate hidden state, the hidden states were varied as 4, 16, 32 and 64, and the models were trained. The experiments indicated that the increase in hidden state improves the performance of the model. Among all the combinations, Bi-LSTM with 64 hidden states displayed the best accuracy (94%). For Tamil POS tagging, this is the initial attempt to be carried out using a deep learning model.

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Section: Related Workmentioning

confidence: 99%