https://www.bibsonomy.org/tag/relation_extractionBibSonomy RSS Feed for /tag/relation_extractionT-Rex (Trainable Relation Extraction) is a highly configurable machine learning-based Information Extraction from Text framework, which includes tools for document classification, entity extraction and relation extraction. http://sourceforge.net/projects/t-rex/hkorte2010-04-26T13:29:51+02:00named_entity_recognition relation_extraction tools <span itemprop="description">T-Rex (Trainable Relation Extraction) is a highly configurable machine learning-based Information Extraction from Text framework, which includes tools for document classification, entity extraction and relation extraction. </span>RelEx, a narrow-AI component of OpenCog, is an English-language semantic relationship extractor, built on the Carnegie-Mellon link parser. It can identify subject, object, indirect object and many other dependency relationships between words in a sentence; it generates dependency trees, resembling those of dependency grammars.http://opencog.org/wiki/RelEx_Semantic_Relationship_Extractorhkorte2009-02-01T14:39:44+01:00opensource relation_extraction tools <span itemprop="description">RelEx, a narrow-AI component of OpenCog, is an English-language semantic relationship extractor, built on the Carnegie-Mellon link parser. It can identify subject, object, indirect object and many other dependency relationships between words in a sentence; it generates dependency trees, resembling those of dependency grammars.</span>The objective of the ACE Program is to develop extraction technology to support automatic processing of source language data (in the form of natural text, and as text derived from ASR and OCR). This includes classification, filtering, and selection based on the language content of the source data, i.e., based on the meaning conveyed by the data. Thus the ACE program requires the development of technologies that automatically detect and characterize this meaning. The ACE research objectives are viewed as the detection and characterization of Entities, Relations, and Events.http://projects.ldc.upenn.edu/ace/hkorte2008-10-28T22:04:44+01:00data_source nlp relation_extraction <span itemprop="description">The objective of the ACE Program is to develop extraction technology to support automatic processing of source language data (in the form of natural text, and as text derived from ASR and OCR). This includes classification, filtering, and selection based on the language content of the source data, i.e., based on the meaning conveyed by the data. Thus the ACE program requires the development of technologies that automatically detect and characterize this meaning. The ACE research objectives are viewed as the detection and characterization of Entities, Relations, and Events.</span>Semantic MediaWiki (SMW) is a free extension of MediaWiki that helps to search, organise, tag, browse, evaluate, and share the wiki's content. While traditional wikis contain only texts which computers can neither understand nor evaluate, SMW adds semantic annotations that bring the power of the Semantic Web to the wiki. http://semantic-mediawiki.org/wiki/Help:Introduction_to_Semantic_MediaWikihkorte2008-10-08T15:34:35+02:00information_extraction rdf relation_extraction semantics wiki <span itemprop="description">Semantic MediaWiki (SMW) is a free extension of MediaWiki that helps to search, organise, tag, browse, evaluate, and share the wiki&#039;s content. While traditional wikis contain only texts which computers can neither understand nor evaluate, SMW adds semantic annotations that bring the power of the Semantic Web to the wiki. </span>Tasks and data of SemEval 2007http://nlp.cs.swarthmore.edu/semeval/tasks/index.phphkorte2008-09-30T15:48:43+02:00conference nlp relation_extraction <span itemprop="description">Tasks and data of SemEval 2007</span>