Boosting Afaan Oromo Named Entity Recognition with Multiple Methods
Автор: Abdo Ababor Abafogi
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 5 vol.13, 2021 года.
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Named Entity Recognizer (NER) is a widely used method of Information extraction (IE) in Natural language processing (NLP) and Information Retrieval (IR) aimed at predicting and categorizing words of a given text into predefined classes of Named Entities like a person, date/time, organization, location, etc. This paper adopts boosting NER for Afaan Oromo by using multiple methods. Combinations of approaches such as machine learning, the stored rules, and pattern matching make a system more efficient and accurate to recognize candidates name entities (NEs). It takes the strongest points from each method to boost the system performance by voting a candidate NE which is detected in more than 1 entity category or out of context because of word ambiguity, it penalized by Word senses disambiguation. Subsequent NEs tagged with identical tags merged as a single tag before the final output. The evaluation shows the system is outperformed. Finally, the future direction is forwarded a hybrid approach of rule-based with unsupervised zero-resource cross-lingual to enhance more.
Afaan Oromo, Named Entity Recognition, Word Sense Disambiguation, NLP, Information Extraction
Короткий адрес: https://sciup.org/15017798
IDR: 15017798 | DOI: 10.5815/ijieeb.2021.05.05
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