IDIOMATIC EXPRESSION IDENTIFICATION FROM AMHARIC  TEXTS USING DEEP LEARNING

TIRUEDLE, ASTERAYE TSIGE

dc.contributor.author	TIRUEDLE, ASTERAYE TSIGE
dc.date.accessioned	2022-12-31T06:51:02Z
dc.date.available	2022-12-31T06:51:02Z
dc.date.issued	2022-07
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/14789
dc.description.abstract	Idiomatic expressions are a natural feature of all languages and are used frequently in our daily conversations. Idioms are difficult to comprehend because their meaning cannot be deduced immediately from the phrase from which they are derived. Idiomatic expressions are phrases or clauses that have a common meaning that is unrelated to the individual words' meanings. The existence of idiomatic expression in a text has an impact on different natural language processing studies like Machine translation, sentiment analysis, and semantics analysis are affected by idiom. Because idioms are one of the most important aspects of a language, having an algorithm and mechanism for detecting them is critical for NLP research. Several scholars conduct idiom recognition in different languages however, there is not enough work on Amharic language idiomatic expression identification. Models which are developed for other languages are not effective to process Amharic documents due to morphological, syntactical, and semantic differences. So, to address those gaps we developed a model for identifying the idiomatic expressions from Amharic texts. This study used a deep learning approach to design an idiomatic expression identification model and the corresponding idiomatic meaning for the Amharic language using the Anaconda Jupiter notebook python framework. We have experimented with CNN, LSTM, and BiLSTM deep learning algorithms using 4800 Amharic sentences for idiomatic expression identification. The accuracy of CNN, LSTM, and BiLSTM was 94%, 95%, and 99% respectively. We compared the proposed model with classical machine learning algorithms SVM and KNN. The experimental result shows that the proposed BiLSTM model performs better than SVM, KNN, CNN, and LSTM. Keywords: Amharic Idiomatic Expression, Natural Language Processing, Deep Learning	en_US
dc.language.iso	en_US	en_US
dc.subject	INFORMATION TECHNOLOGY	en_US
dc.title	IDIOMATIC EXPRESSION IDENTIFICATION FROM AMHARIC TEXTS USING DEEP LEARNING	en_US
dc.type	Thesis	en_US