COMPARATIVE ANALYSIS OF VARIOUS APPROACHES BASED ON NAMED ENTITY RECOGNITION-A SURVEY

Sakshi ., Shailendra Singh, Dharam Vir, Shashank Singh

Abstract


Extraction of advantageous information from the data has turned out to be the most decisive activity across all domains because of the increase in the availability of data. Information Extraction goes into the more challenging task due to the availability of data in the form of documents written in the natural language. Named Entity Recognition (NER) is the part of Information Extraction which is used to extract important information from the code- mixed and informal data and then classifies these extracting named entities into its pre-characterized classes. For example: person, location, organization, city, state, and country etc. NER is acknowledged as the dominant task in the field of Natural Language Processing (NLP). This paper provides a survey of various methods and techniques which are being used in the extraction of proper nouns appeared in the document. This paper also outlines the knowledge of various challenges which are being faced while extracting the named entities. It also provides some research directions which various researchers can explore.             

Keywords


Entity Extraction, Machine Learning, Linguistic approaches, Named Entity Recognition.

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References


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DOI: https://doi.org/10.26483/ijarcs.v9i3.6093

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