Named Entity Recognition for Afan Oromo
Developing Named Entity Recognition for Resource Scarce Languages
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Named Entity Recognition (NER) is an information extraction task aimed at identifying and classifying words of a sentence, a paragraph or a document into predefined categories of Named Entities (NEs). NEs are terms that are used to name a person, location or organization. They are also used to refer to the value or amount of something. NER is an important tool in almost all NLP application areas out of which it is very essential in Search Engines (Semantic based), Machine Translation, Question-Answering, Indexing for Information Retrieval and Automatic Summarization systems. A lot of NER resea...
Named Entity Recognition (NER) is an information extraction task aimed at identifying and classifying words of a sentence, a paragraph or a document into predefined categories of Named Entities (NEs). NEs are terms that are used to name a person, location or organization. They are also used to refer to the value or amount of something. NER is an important tool in almost all NLP application areas out of which it is very essential in Search Engines (Semantic based), Machine Translation, Question-Answering, Indexing for Information Retrieval and Automatic Summarization systems. A lot of NER researches have been conducted and systems have been developed for a resource rich European and Asian languages. This book proposes and presents the development of NER system for Afan Oromo, a language that has the largest native speakers in Ethiopia. The algorithms and techniques presented in this study have shown good performance thereby reflecting how NER system can be developed for a resourcescarce languages.
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