Text Mining Export

@incollection{citeulike:3152878, abstract = {One of the fastest-growing fields in bioinformatics is text mining: the application of natural language processing techniques to problems of knowledge management and discovery, using large collections of biological or biomedical text such as MEDLINE. The techniques used in text mining range from the very simple (e.g., the inference of relationships between genes from frequent proximity in documents) to the complex and computationally intensive (e.g., the analysis of sentence structures with parsers in order to extract facts about protein —protein interactions from statements in the text). This chapter presents a general introduction to some of the key principles and challenges of natural language processing, and introduces some of the tools available to end-users and developers. A case study describes the construction and testing of a simple tool designed to tackle a task that is crucial to almost any application of text mining in bioinformatics —identifying gene/protein names in text and mapping them onto records in an external database.}, author = {Clegg, Andrew B. and Shepherd, Adrian J.}, citeulike-article-id = {3152878}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-60327-429-6_25}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18712320}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18712320}, citeulike-linkout-3 = {http://www.springerlink.com/content/g864wv1j02744r17}, doi = {10.1007/978-1-60327-429-6_25}, journal = {Bioinformatics}, keywords = {bioinformatics, knowledge\_management, natural\_language\_processing, text\_mining}, pages = {471--491}, posted-at = {2009-10-28 23:52:30}, priority = {2}, title = {Text Mining}, url = {http://dx.doi.org/10.1007/978-1-60327-429-6_25}, year = {2008} }

TY - CHAP ID - citeulike:3152878 L3 - citeulike-article-id:3152878 N2 - One of the fastest-growing fields in bioinformatics is text mining: the application of natural language processing techniques to problems of knowledge management and discovery, using large collections of biological or biomedical text such as MEDLINE. The techniques used in text mining range from the very simple (e.g., the inference of relationships between genes from frequent proximity in documents) to the complex and computationally intensive (e.g., the analysis of sentence structures with parsers in order to extract facts about protein —protein interactions from statements in the text). This chapter presents a general introduction to some of the key principles and challenges of natural language processing, and introduces some of the tools available to end-users and developers. A case study describes the construction and testing of a simple tool designed to tackle a task that is crucial to almost any application of text mining in bioinformatics —identifying gene/protein names in text and mapping them onto records in an external database. JF - Bioinformatics EP - 491 TI - Text Mining SP - 471 KW - bioinformatics KW - knowledge_management KW - natural_language_processing KW - text_mining AU - Clegg, Andrew AU - Shepherd, Adrian PY - 2008/// UR - http://dx.doi.org/10.1007/978-1-60327-429-6_25 ER -