
Data Mining
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GATE.ac.uk - teamware/teamware-detail.html
Semantic Annotation (SA) is about attaching meaningful structures to resources like documents or video streams in such a way that they can be used by computers to enhance the usefulness of those resources. SA is not new: when a BBC archivist, for example, attaches thesaurus categories to programme segments for indexing they have performed semantic annotation.GATE.ac.uk - teamware/index.html
GATE.ac.uk - index.html
NOTE: if you are upgrading from one version of GATE to another you must delete your user configuration file before running the new version. The user configuration file is typically ~/.gate.xml on Unix-style platforms (including Mac OS X), C: \ Documents and Settings \ username \ gate.xml on Windows XP, and C: \ Users \ username \ gate.xml on Windows Vista or later. Release 7.0 (February 8th 2012)
GATE.ac.uk - download/index.html
GATE.ac.uk - family/process.html
The GATE Process describes the steps you need to take if you want to create predictable and sustainable language processing capabilities in your organisation. The process is supported by software (most notably GATE Teamware ), but it is not primarily based on tools.UIMA - Wikipedia, the free encyclopedia
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Data mining - Wikipedia, the free encyclopedia
Data mining (the analysis step of the knowledge discovery in databases process, [ 1 ] or KDD), a relatively young and interdisciplinary field of computer science [ 2 ] [ 3 ] is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence , machine learning , statistics and database systems . [ 2 ] The overall goal of the data mining process is to extract knowledge from a data set in a human-understandable structure [ 2 ] and besides the raw analysis step involves database and data management aspects, data preprocessing , model and inference considerations, interestingness metrics, complexity considerations, post-processing of found structure, visualization and online updating . [ 2 ]The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.

