A STUDY ON COMBINING INFORMATION EXTRACTION AND NATURAL LANGUAGE PROCESSING WITH TEXT MINING

Abstract

The problem of text mining, is discovering useful knowledge from unstructured text, is attracting increasing attention. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods. This technology is now broadly applied for a wide variety of government, research and business needs like security, biomedical, software, online media, marketing, sentiment analysis and academic applications. There are many software’s available in the market that has made text mining easier. There are still researches going on to make new advancements in this field. An important approach to text mining involves the use of natural-language information extraction. Information extraction (IE) distills structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. Information extraction systems can be used to directly extricate abstract knowledge from a text corpus, or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns