Neural Network based Parts of Speech Tagger for Hindi

Narayan, Ravi; Chakraverty, Snehashish; Singh, Varinder

doi:10.3182/20140313-3-in-3024.00041

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…By using RBA model, authors got 76% and 80% accuracy for sports information dataset and amusement dataset respectively. Sharma et al [18] [20] developed Hindi POS tagger using Quantum Neural Network (QNN) and achieved 99.13% accuracy. Mohnot et al [21] proposed Hindi POS tagger developed using Hybrid Approach (HA) and it could be the combination of RBA, CRF, HMM and so on.…”

Section: Related Workmentioning

confidence: 99%

On State-of-the-art of POS Tagger, ‘Sandhi’ Splitter, ‘Alankaar’ Finder and ‘Samaas’ Finder for Indo-Aryan and Dravidian Languages

Gaikwad¹,

Jatinderkumar²

2021

IJACSA

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Computational Linguistic refers to the development of the computer systems that deal with human languages. In this paper, different Computational Linguistic Techniques such as Parts of Speech (POS) tagger, 'Sandhi' Splitter, 'Alankaar' Finder and 'Samaas' Finder were considered. After a thorough literature review, it was found that fifteen techniques were used for POS tagging, nine techniques were used for 'Sandhi' splitting, one work is done for 'Alankaar' finder and absolutely no techniques are available for 'Samaas' finder for the Indo-Aryan as well as Dravidian languages. Analysis shows that Rule Based Approach (RBA) and Hidden Markov Model (HMM) are frequently used for POS tagging, RBA is most frequently used for 'Sandhi' Splitter, the general Human Intelligence (HI) is used for 'Alankaar' Finder and no 'Samaas' finder technique is available for any Indian language.

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Section: Related Workmentioning

confidence: 99%

On State-of-the-art of POS Tagger, ‘Sandhi’ Splitter, ‘Alankaar’ Finder and ‘Samaas’ Finder for Indo-Aryan and Dravidian Languages

Gaikwad¹,

Jatinderkumar²

2021

IJACSA

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“…This is an essential step in many natural language processing (NLP) applications. For the past decade, part-of-speech tagging been developed using various methods and for many languages [1,2]. These methods show different results on languages because every language has its own peculiarity.…”

Section: Introductionmentioning

confidence: 99%

“…Cyrillic script is widely used, whereas the traditional script remains in some other places, for instance, inner Mongolia autonomous region. 1 The root of Mongolian is Altaic language family. Also, many researchers agree that Mongolian is related to Turkish and Korean [7].…”

Section: Introductionmentioning

confidence: 99%

Mongolian Part-of-Speech Tagging with Neural Networks

Lkhagvasuren

Rentsendorj

Namsrai

2021

Advances in Intelligent Information Hiding and Multimedia Signal Processing

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The purpose of this paper is to present a simple neural networks model-multilayer perceptron for Mongolian part-of speech tagging. We used about 1400 manually tagged sentences for training and testing from Mongolian Penn Treebank. The performance of the model is 80.78% which we consider a promising result. Also, another contribution of this work is that we make our testing data online for the sake of the development of Mongolian part-of-speech tagging research.

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