Mining knowledge from text using information extraction Export

@article{citeulike:342625, abstract = {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. IE 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. We discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text.}, address = {New York, NY, USA}, author = {Mooney, Raymond J. and Bunescu, Razvan C.}, citeulike-article-id = {342625}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1089817}, citeulike-linkout-1 = {http://dblp.uni-trier.de/rec/bibtex/journals/sigkdd/MooneyB05}, citeulike-linkout-2 = {http://dx.doi.org/10.1145/1089815.1089817}, doi = {10.1145/1089815.1089817}, issn = {1931-0145}, journal = {SIGKDD Explorations}, keywords = {information-extraction, knowledge-engineering, natural\_language, text-mining}, month = {June}, number = {1}, pages = {3--10}, posted-at = {2009-10-14 03:12:17}, priority = {2}, publisher = {ACM}, title = {Mining knowledge from text using information extraction}, url = {http://dx.doi.org/10.1145/1089815.1089817}, volume = {7}, year = {2005} }

TY - JOUR ID - citeulike:342625 L3 - citeulike-article-id:342625 N2 - 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. IE 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. We discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text. IS - 1 JF - SIGKDD Explorations SN - 1931-0145 AD - New York, NY, USA EP - 10 TI - Mining knowledge from text using information extraction PB - ACM VL - 7 SP - 3 KW - information-extraction KW - knowledge-engineering KW - natural_language KW - text-mining AU - Mooney, Raymond AU - Bunescu, Razvan PY - 2005/06// UR - http://dx.doi.org/10.1145/1089815.1089817 ER -