Abstract
With the advancements of new technologies, a large volume of digital data is getting generated every second from various internal and external sources like social networking, organizations and any business applications. Big data refers to enormous digital data that are high in volume, velocity, varieties. The traditional conventional approach fails to handle large data sets using their tools and techniques. Big data proved to be an effective mean for collecting, analyzing and processing data despite their size and data formats structured, semi-structured or unstructured. Large set of information and data are produced from different organizations and social activities. Text mining or text analytics plays a significant role in deriving relevant information from text in digital environment. Text mining includes technique like entity extraction which automatically extracts structured information from unstructured or semi-structured documents. This paper details how entity extraction is useful in processing human language texts by using natural language processing. Entity extraction based on method like part-of-speech tagging which helps in determining the noun, verb, adverb and adjectives associated with a sentence. Enhanced entity extraction method will be mainly useful for filtering entities based on their part-of-speeches by removing any ambiguities. Entity extraction focuses on relevant parts of a document and represents them in a structured manner.
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Dash, A., Pandey, M., Rautaray, S. (2019). Enhanced Entity Extraction Using Big Data Mechanics. In: Kamal, R., Henshaw, M., Nair, P. (eds) International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol 870. Springer, Singapore. https://doi.org/10.1007/978-981-13-2673-8_8
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DOI: https://doi.org/10.1007/978-981-13-2673-8_8
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