A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A9P3Y66JI" target="_blank" >RIV/00216208:11320/23:9P3Y66JI - isvavai.cz</a>
Výsledek na webu
<a href="https://www.researchsquare.com/article/rs-3274325/latest" target="_blank" >https://www.researchsquare.com/article/rs-3274325/latest</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21203/rs.3.rs-3274325/v1" target="_blank" >10.21203/rs.3.rs-3274325/v1</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text
Popis výsledku v původním jazyce
"Part-of-speech tagging faces unique difficulties when dealing with code-mixed social media text, which combines multiple languages in informal content created by users. In India, many web users employ a mixture of regional languages and English on platforms like Facebook, Instagram, and WhatsApp to express their messages and emotions. Text derived from social media is used in a variety of applications, such as speech recognition, machine learning, information retrieval, question answering, sentiment analysis, and named entity recognition. Due to training with monolingual texts, natural language processing tools such as part-of-speech taggers and parsers don't perform well. Assigning grammatical labels to individual words (such as verbs, adjectives, and nouns) is a critical task in natural language processing. This review paper extensively surveys the existing literature on part-of-speech tagging specifically developed for Indian and Foreign code-mixed social media text."
Název v anglickém jazyce
A Comprehensive Survey of Techniques Used for Part-of-Speech Tagging of Code-Mixed Social Media Text
Popis výsledku anglicky
"Part-of-speech tagging faces unique difficulties when dealing with code-mixed social media text, which combines multiple languages in informal content created by users. In India, many web users employ a mixture of regional languages and English on platforms like Facebook, Instagram, and WhatsApp to express their messages and emotions. Text derived from social media is used in a variety of applications, such as speech recognition, machine learning, information retrieval, question answering, sentiment analysis, and named entity recognition. Due to training with monolingual texts, natural language processing tools such as part-of-speech taggers and parsers don't perform well. Assigning grammatical labels to individual words (such as verbs, adjectives, and nouns) is a critical task in natural language processing. This review paper extensively surveys the existing literature on part-of-speech tagging specifically developed for Indian and Foreign code-mixed social media text."
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
—
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů