A comparison of features for POS tagging in Kannada

Shriya, Atmakuri,; Shahi, Bhavya; Rao, Ashwath B; Muralikrishna, S.

doi:10.14419/ijet.v7i4.14900

Cited by 6 publications

(2 citation statements)

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“…POS tags are assigned after analyzing each word in the text, authors have demonstrated POS tagging [26] using machine learning algorithms and deep learning algorithms. One of the machine learning algorithms SVM outperforms deep learning techniques with 85% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

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Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

Chandrika¹,

Kallimani²

2022

IJACSA

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Author attribution is the field of deducing the author of an unknown textual source based on certain characteristics inherently present in the author's style of writing. Author attribution has a ton of useful applications which help automate manual tasks. The proposed model is designed to predict the authorship of the Kannada text using a sequential neural network with Bi-Directional Long Short Term Memory layers, Dense layers, Activation function and Dropout layers. Based on the nature of the data, we have used stochastic gradient descent as an optimizer that improves the learning of the proposed model. The model extracts Part of the speech tags as one of the semantic features using the N-gram technique. A Conditional random fields model is developed to assign Part of the speech tags for the Kannada text tokens, which is the base for the proposed model. The parts of the speech model achieve an overall 90% and 91% F1 score and accuracy respectively. There is no state-of-art model to compare the performance of our model with other models developed for the Kannada language. The proposed model is evaluated using the One Versus Five (1 vs 5) method and overall accuracy of 77.8% is achieved.

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Section: Literature Surveymentioning

confidence: 99%

“… Pass the tokens to a POS tagger developed using the CRF model [25,26]. For the sake of better results, the number of parts of speech is reduced and more generalized, then run the bag of words code into the POS tagger and assign a tag to each word.…”

Section: A Dataset Source and Collectionmentioning

confidence: 99%