International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 13, No. 5, 8 Oct. 2021
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Boosting Afaan Oromo Named Entity Recognition with Multiple Methods
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Author(s)
Index Terms
Afaan Oromo, Named Entity Recognition, Word Sense Disambiguation, NLP, Information Extraction
Abstract
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.
Cite This Paper
Abdo Ababor Abafogi, "Boosting Afaan Oromo Named Entity Recognition with Multiple Methods", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.13, No.5, pp. 51-59, 2021. DOI:10.5815/ijieeb.2021.05.05
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