Scopus Indexed Publications
Paper Details
- Title
-
Performance Measurement of Multiple Supervised Learning Algorithms for Gender Identification from Bengali Names
- Author
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Labannya Saha,
Rakib Md. Azhar Uddin,
- Email
-
- Abstract
-
Gender recognition of names is
today regarded as one of the most significant problems in data
extraction. Machine learning and NLP are used to solve this. Recognition
of the gender of a name relies heavily on various kinds of NLP
applications such as Question addressing systems, Text summarization
etc. Throughout this article, we will investigate the Bengali name
entity and attempt to extract and recognize the Person's name entity to
determine if the name is male or female. Most people's names have a
sexual orientation refinement. However, determining genders from Bengali
names with greater accuracy might be difficult. We will demonstrate an
artificial intelligence-based characterization technique that can
accurately determine sexual orientations from Bengali names. We'll use a
variety of algorithms to figure out which computations get the best
results. We attained an accuracy of 84 percent by using Naive Bayes.
- Keywords
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Gender Recognition , Machine Learning , NLP , Naive Bayes
- Journal or Conference Name
- 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)
- Publication Year
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2021
- Indexing
-
scopus