A Hybrid Statistical Approach for Named Entity Recognition for Amazighe Language

Recognition of named entities (NEs) from computer readable natural language text is significant task of information extraction (IE) and natural language processing (NLP). Named entity (NE) extraction is important step for processing unstructured content. Unstructured data is computationally opaque. Computers require computationally transparent data for processing. There are various different approaches that are applied for extraction of entities from text. Named entity recognition (NER)is probably the first step towards information extraction that seeks to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. NER is used in many fields in NLP, and it can help resolving many real NLP tasks.This paper elaborates need of NE recognition and discusses issues and challenges involved in NE recognition tasks for Amazighe language. It also explores various methods and techniques that are useful for creation of learning resources and lexicons that are important for extraction of NEs from natural language unstructured text.