Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language
Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, an...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/4429069 |
| _version_ | 1797249389390987264 |
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| author | Tabor Wegi Geleta Jara Muda Haro |
| author_facet | Tabor Wegi Geleta Jara Muda Haro |
| author_sort | Tabor Wegi Geleta |
| collection | DOAJ |
| description | Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems. |
| first_indexed | 2024-04-24T20:29:42Z |
| format | Article |
| id | doaj.art-ddf35bbfc5774050aa0292621a0cce79 |
| institution | Directory Open Access Journal |
| issn | 1687-9732 |
| language | English |
| last_indexed | 2024-04-24T20:29:42Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj.art-ddf35bbfc5774050aa0292621a0cce792024-03-22T00:00:03ZengHindawi LimitedApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/4429069Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo LanguageTabor Wegi Geleta0Jara Muda Haro1Department of Information ScienceDepartment of Information TechnologyNatural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.http://dx.doi.org/10.1155/2024/4429069 |
| spellingShingle | Tabor Wegi Geleta Jara Muda Haro Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language Applied Computational Intelligence and Soft Computing |
| title | Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language |
| title_full | Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language |
| title_fullStr | Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language |
| title_full_unstemmed | Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language |
| title_short | Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language |
| title_sort | semisupervised learning based word sense disambiguation using word embedding for afaan oromoo language |
| url | http://dx.doi.org/10.1155/2024/4429069 |
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