Context-Based Bigram Model for POS Tagging in Hindi: A Heuristic Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ANXJGVCU7" target="_blank" >RIV/00216208:11320/23:NXJGVCU7 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s40745-022-00434-4" target="_blank" >https://doi.org/10.1007/s40745-022-00434-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40745-022-00434-4" target="_blank" >10.1007/s40745-022-00434-4</a>

Result language

angličtina

Original language name

Context-Based Bigram Model for POS Tagging in Hindi: A Heuristic Approach

Original language description

"In the domain of natural language processing, part-of-speech (POS) tagging is the most important task. It plays a vital role in applications like sentiment analysis, text summarization, opinion mining, etc. POS tagging is a process of assigning POS information (noun, pronoun, verb, etc.) to the given word. This information is considered in the context of their relationship with the surrounding words. Hindi is very popular language in countries like India, Nepal, United States, Mauritius, etc. Majority of Indians are accustomed to Hindi for reading and writing. They also use Hindi for writing on social media such as Twitter, Facebook, WhatsApp, etc. POS tagging is the most important phase to analyze these Hindi text from social media. The text scripted in Hindi is ambiguous in nature and rich in morphology. It makes identification of POS information challenging. In this article, a heuristic based approach is proposed for identifying POS information. The proposed method deployed a context-based bigram model that create a bigram sequence based on the relationship with the adjacent words. Subsequently, it selects the most likelihood POS information for a word based on both the forward and reverse bigram sequences. The experimental result of the proposed heuristic approach is compared with existing state-of-the-art techniques like hidden Markov model, decision tree, conditional random fields, support vector machine, neural network, and recurrent neural networks. Finally, it is observe that the proposed heuristic approach for POS tagging in Hindi outperforms the existing techniques and attains an accuracy of 94.3%."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"Annals of Data Science"

ISSN

2198-5812

e-ISSN

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Volume of the periodical

""

Issue of the periodical within the volume

2023

Country of publishing house

US - UNITED STATES

Number of pages

32

Pages from-to

347-378

UT code for WoS article

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EID of the result in the Scopus database

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