Modelings and techniques in named entity recognition: an information extraction task

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Abstract

Information Extraction, which is an area of natural language processing that deals with finding factual information in free text. The task of Information Extraction (IE) is to identify a predefined set of concepts i.e., a representation of the information that is machine understandable. The classic IE tasks include Named Entity Recognition (NER) addresses the problem of the identification and classification of predefined types of named entities. Co-reference Resolution (CO), Relation Extraction (RE),Event Extraction (EE) Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. In this paper, we present various efficient NER techniques and modeling's . Named entity recognition addresses the problem of locating textual mentions of predefined types of entities, where the entity categories can be very diverse, ranging from people and companies in business applications to cells and proteins in biomedical applications.

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