POS Tagger Model for South Indian Language Using a Deep Learning Approach

Shree, M. Rajani; Shambhavi, B. R.

doi:10.1007/978-981-16-7985-8_16

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The authors used deep learning methodologies [24] like RNN and LSTM to assign POS tags to the annotated Kannada words and achieved 81% accuracy. The limitation of this work is in getting the clear dataset in the required format since the same words are spoken and written in different ways due to this one word can have different inflections.…”

Section: Literature Surveymentioning

confidence: 99%

“…The proposed work is implemented based on the POS and sequence model. The AA is based on the POS tagging to extract the hidden semantic meaning of the text, since there is no open access POS tagging application available on the internet, we created a POS model [24], and the summary of the overall implementation is briefed below:  Preprocess the dataset by tokenizing the documents (articles/stories/poems) of each author and creating an array of sentences.…”

Section: A Dataset Source and Collectionmentioning

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%

Section: A Dataset Source and Collectionmentioning

confidence: 99%

Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

Chandrika¹,

Kallimani²

2022

IJACSA

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Deep Learning based Part-of-Speech tagging for Assamese using RNN and GRU

Talukdar,

Sarma

2024

Procedia Computer Science

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An Analysis of Deep Learning Techniques Adopted in Medical Imaging

Rajani Shree,

Bhatta,

Sarveshvar

et al. 2023

Handbook of Research on Advancements in AI and IoT Convergence Technologies

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The various methods which have been adopted in processing and segmentation of medical images using deep learning and machine learning techniques are examined and analyzed in this article. Medical images analysis and their methods have been swiftly evolved into deep learning techniques and especially in convolutional neural networks. Deep learning concepts that may be used for the image classification, detection, and logging of medical related pictures and objects have been examined. Medical applications include: research and investigations into neuro, retinal and pulmonary, digital pathologies, breast, heart and musculoskeletal diseases and their corresponding analysis. Deep learning has already been used to accurately diagnose diseases and classify image samples, and it has the potential to revolutionise the entire landscape of healthcare. These uses are only expected to expand in the future. The most recent developments, including a critical analysis of the current problems have been summarized, and made plans for additional research in medical imaging.

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POS Tagger Model for South Indian Language Using a Deep Learning Approach

Cited by 3 publications

References 15 publications

Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

Authorship Attribution on Kannada Text using Bi-Directional LSTM Technique

Deep Learning based Part-of-Speech tagging for Assamese using RNN and GRU

An Analysis of Deep Learning Techniques Adopted in Medical Imaging

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