Earnest Erihbra | University Of Lagos Akoka (original) (raw)
I am an auto-didactic software developer with a keen interest in delivering professional services to suit the taste of the customers by carrying out programming tasks including enhancements, maintenance and support for clients’ applications and interfaces. Engineering software products, handling complex analysis,designs and implementations to meet project requirements.A graduate of the department of Computer Sciences,University of Lagos,Nigeria with a strong passion for personal excellence and advancement,which is the core driving force behind my thirst for knowledge.I am very much interested in Fin.Tech,Machine Learning/AI and open to volunteer opportunities to Speak in Conferences geared towards helping teenagers/youths discover who they really are and maximize their potentials!I have Strong Teaching Gift and excellent at communicating to people across different levels/strata of life.
Address: Lagos, Lagos, Nigeria
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Papers by Earnest Erihbra
The current proliferation of text data on the Internet in different languages spoken across the g... more The current proliferation of text data on the Internet in different languages spoken across the globe calls for a need to develop intelligent systems that can help in recognising the language in which particular texts are written for proper functioning of more complex tasks such as Language translation.
Language identification, the process of determining the natural language in which a text document is written has always been a pivotal research in the areas of text mining and natural language processing.
In the literatures, several statistical models have been reported for solving text language identification problem such as n-gram model, modified n-gram models etc. In this project work, a machine learning approach to language identification was used. Two supervised learning algorithms which are the multinomial Naïve Bayes and k-Nearest Neighbours algorithm were implemented for text language classification.
The training and test data were gotten using Wikipedia’s Multilanguage features and also from various other sources on the Internet. The implementation was done in Java. The classifiers were trained to recognize different languages, three of which are local and recognized Nigerian languages (i.e. Hausa, Igbo and Yoruba).Performance comparison of the two algorithms in terms of speed and prediction accuracy under different working conditions was carried out and it was discovered that the Naïve Bayes classifier out performs the K-Nearest Neighbours algorithm.
The current proliferation of text data on the Internet in different languages spoken across the g... more The current proliferation of text data on the Internet in different languages spoken across the globe calls for a need to develop intelligent systems that can help in recognising the language in which particular texts are written for proper functioning of more complex tasks such as Language translation.
Language identification, the process of determining the natural language in which a text document is written has always been a pivotal research in the areas of text mining and natural language processing.
In the literatures, several statistical models have been reported for solving text language identification problem such as n-gram model, modified n-gram models etc. In this project work, a machine learning approach to language identification was used. Two supervised learning algorithms which are the multinomial Naïve Bayes and k-Nearest Neighbours algorithm were implemented for text language classification.
The training and test data were gotten using Wikipedia’s Multilanguage features and also from various other sources on the Internet. The implementation was done in Java. The classifiers were trained to recognize different languages, three of which are local and recognized Nigerian languages (i.e. Hausa, Igbo and Yoruba).Performance comparison of the two algorithms in terms of speed and prediction accuracy under different working conditions was carried out and it was discovered that the Naïve Bayes classifier out performs the K-Nearest Neighbours algorithm.