Boosting Afaan Oromo Named Entity Recognition with Multiple Methods
Abdo Ababor Abafogi · International Journal of Information Engineering and Electronic Business · 2021
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.