Fast and Effective System for Name Entity Recognition on Big Data - Nigam, Jigyasa; Sahu, Sandeep (International Journal of Computer Sciences and Engineering, 2015)
In today scenario all data store in digital form and data size is too large. So problem is that how to manage this big data or extract information with speed and efficiency. Information extraction is a technique which using in text mining. Information extraction extract required information whose user demand from unstructured text. Information extraction use NLP (Natural Language Processing) and NER (Name entity recognition). NER systems help to machine recognize proper noun (entity), events, relationships and so on. There are several NER systems in the world. Such as GATE, CRFClassifier, OpenNLP and Stanford NLP (Natural Language Processing ). The NER system works fast for limited amount of documents but drawback of this system is that it works slows for huge/large amount of data. To overcome the drawback of NER system, this paper, report the implement of a NER which is based on Map Reduce, a distributed programming model. This improvement helps to achieve the fast extraction and reduce storage cost with better performance.