Complexity Metric for Code-Mixed Social Media Text



Document title: Complexity Metric for Code-Mixed Social Media Text
Journal: Computación y Sistemas
Database: PERIÓDICA
System number: 000423300
ISSN: 1405-5546
Authors: 1
2
3
Institutions: 1Rutgers University, New Brunswick, New Jersey. Estados Unidos de América
2MAKAUT, Calcuta, Bengala Occidental. India
3Jadavpur University, Calcuta, Bengala Occidental. India
Year:
Season: Oct-Dic
Volumen: 21
Number: 4
Country: México
Language: Inglés
Document type: Artículo
Approach: Aplicado, descriptivo
English abstract An evaluation metric is an absolute necessity for measuring the performance of any system and complexity of any data. In this paper, we have discussed how to determine the level of complexity of code-mixed social media texts that are growing rapidly due to multilingual interference. In general, texts written in multiple languages are often hard to comprehend and analyze. At the same time, in order to meet the demands of analysis, it is also necessary to determine the complexity of a particular document or a text segment. Thus, in the present paper, we have discussed the existing metrics for determining the code-mixing complexity of a corpus, their advantages and shortcomings as well as proposed several improvements on the existing metrics. The new index better reflects the variety and complexity of a multilingual document. Also, the index can be applied to a sentence and seamlessly extended to a paragraph or an entire document. We have employed two existing code-mixed corpora to suit the requirements of our study
Disciplines: Ciencias de la computación,
Literatura y lingüística
Keyword: Lingüística aplicada,
Procesamiento de lenguaje natural,
Lingüística computacional,
Cienciometría,
Mezcla de códigos,
Transliteración,
Redes sociales,
Indice de complejidad
Keyword: Applied linguistics,
Natural language processing,
Computational linguistics,
Scientometrics,
Code mixing,
Transliteration,
Social networks,
Complexity index
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