Information extraction in text mining
Matt Mulins · Western CEDAR (Western Washington University) · 2008
Text mining’s goal, simply put, is to derive information from text. Using multitudes of technologies from overlapping fields like Data Mining and Natural Language Processing we can yield knowledge from our text and facilitate other processing. Information Extraction (IE) plays a large part in text mining when we need to extract this data. In this survey we concern ourselves with general methods borrowed from other fields, with lower-level NLP techniques, IE methods, text representation models, and categorization techniques, and with specific implementations of some of these methods. Finally, with our new understanding of the field we can discuss a proposal for a system that combines WordNet, Wikipedia, and extracted definitions and concepts from web pages into a user-friendly search engine designed for topicspecific knowledge.