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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

"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%."

Název v anglickém jazyce

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

Popis výsledku anglicky

"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%."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

"Annals of Data Science"

ISSN

2198-5812

e-ISSN

—

Svazek periodika

""

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

32

Strana od-do

347-378

Kód UT WoS článku

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EID výsledku v databázi Scopus

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