首页|Studies from Debre Markos University in the Area of Machine Learning Published ( Contextual word disambiguates of Ge’ez language with homophonic using machine le arning)
Studies from Debre Markos University in the Area of Machine Learning Published ( Contextual word disambiguates of Ge’ez language with homophonic using machine le arning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from Debre M arkos University by NewsRx correspondents, research stated, “According to natura l language processing experts, there are numerous ambiguous words in languages.” Our news correspondents obtained a quote from the research from Debre Markos Uni versity: “Without automated word meaning disambiguation for any language, the de velopment of natural language processing technologies such as information extrac tion, information retrieval, machine translation, and others are still challengi ng task. Therfore, this paper presents the development of a word sense disambigu ation model for duplicate alphabet words for the Ge’ez language using corpus-bas ed methods. Because there is no wordNet or public dataset for the Ge’ez language , 1010 samples of ambiguous words were gathered. Afterwards, the words were prep rocessed and the text was vectorized using bag of words, Term Frequency-Inverse Document Frequency, and word embeddings such as word2vec and fastText. The vecto rized texts are then analysed using the supervised machine learning algorithms s uch Naive Bayes, decision trees, random forests, K-nearest neighbor, linear supp ort vector machine, and logistic regression. Bag of words paired with random for ests outperformed all other combinations, with an accuracy of 99.52% .”
Debre Markos UniversityCyborgsEmergi ng TechnologiesMachine LearningNatural Language Processing
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