BibSonomyburst/user/lysander07/WCMHLT2006BibSonomy RSS feed for /user/lysander07/WCMHLT20062012-02-16T18:10:04+01:00http://www.bibsonomy.org/bibtex/2c9c529d170f8e7e91502d94cb2c785b9/lysander07lysander072006-11-06T22:17:53+01:00co-occurence web2.0 WCMHLT2006 datamining socialNetworks <span class="authorEditorList"><a href="/author/Jin">YingZi Jin</a>, <a href="/author/Matsuo">Yutaka Matsuo</a>, and <a href="/author/Ishizuka">Mitsuru Ishizuka</a> </span><em>Proc. of ISWC2006 Workshop on Web Content Mining with Human Language Technology, </em>(<em>2006</em>)Mon Nov 06 22:17:53 CET 2006Proc. of ISWC2006 Workshop on Web Content Mining with Human Language TechnologyExtracting a Social Network among Entities by Web mining2006co-occurence web2.0 WCMHLT2006 datamining socialNetworks http://www.bibsonomy.org/bibtex/2027513ba40abf3789d6e8872fbe07287/lysander07lysander072006-11-06T22:05:31+01:00co-occurence WCMHLT2006 google datamining linguistics <span class="authorEditorList"><a href="/author/Geleijnse">Gijs Geleijnse</a>, <a href="/author/Korst">Jan Korst</a>, and <a href="/author/de Boer">Viktor de Boer</a> </span><em>Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language Technologies, </em>(<em>2006</em>)Mon Nov 06 22:05:31 CET 2006Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language TechnologiesInstance Classification using Co-Occurrences on the Web2006co-occurence WCMHLT2006 google datamining linguistics We present a novel unsupervised approach to mapping artrelated instances (such as music artists and painters) to subjective categories like genre and style. We base our approach on co-occurrences of the two on the web, found with Google. The co-occurrences are found using three methods: by identifying the search engine counts, by analyzing Google excerpts found by querying patterns and by scanning full documents. Per instance, we use the same co-occurrence-based approach to find its nearest neighbors, i.e. the most related instances. These results can be combined in order to create a more reliable classification. We tested and compared the three methods on two different domains: mapping music artists to genres, and painters to art-styles. The results show that the use of related instances indeed improves the precision of the classification. Moreover, the methods with the lowest Google Complexity perform best.http://www.bibsonomy.org/bibtex/2026dd8decd2c8b45f96b0e89838231af/lysander07lysander072006-11-06T21:18:15+01:00WCMHLT2006 linguistics wikipedia <span class="authorEditorList"><a href="/author/Suh">Sangweon Suh</a>, <a href="/author/Halpin">Harry Halpin</a>, and <a href="/author/Klein">Ewan Klein</a> </span><em>Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language technology, </em>(<em>2006</em>)Mon Nov 06 21:18:15 CET 2006Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language technologyExtracting Common Sense Knowledge from Wikipedia2006WCMHLT2006 linguistics wikipedia http://www.bibsonomy.org/bibtex/24b8618d51dbdb4ce24310c1a5b0d3bd8/lysander07lysander072006-11-06T21:15:21+01:00WCMHLT2006 linguistics <span class="authorEditorList"><a href="/author/Yatskevich">Mikalai Yatskevich</a>, <a href="/author/Welty">Chris Welty</a>, and <a href="/author/Murdock">J. William Murdock</a> </span> (<em>2006</em>)Mon Nov 06 21:15:21 CET 2006Proc. of ISWC2006 Web Content Mining with Human Language TechnologyCoreference resolution on RDF Graphs generated from Information Extraction: first results2006WCMHLT2006 linguistics http://www.bibsonomy.org/bibtex/21a6a570c5349e6b17c5de1f53a83ff00/lysander07lysander072006-11-06T21:06:27+01:00ontologyLearning WCMHLT2006 datamining speechanalysis <span class="authorEditorList"><a href="/author/Feng">Donghui Feng</a>, <a href="/author/Shaw">Erin Shaw</a>, and <a href="/author/Hovy">Eduard Hovy</a> </span><em>Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language Technologies, </em>(<em>2006</em>)Mon Nov 06 21:06:27 CET 2006Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language TechnologiesMining and Assessing Discussions on the Web through Speech Act Analysis2006ontologyLearning WCMHLT2006 datamining speechanalysis http://www.bibsonomy.org/bibtex/2267dbeb828a421c2acaadd0eaf4baebe/lysander07lysander072006-11-06T21:01:19+01:00WCMHLT2006 linguistics <span class="authorEditorList"><a href="/author/Shinzato">Keiji Shinzato</a>, <a href="/author/Sekine">Satoshi Sekine</a>, <a href="/author/Yoshinaga">Naoki Yoshinaga</a>, and <a href="/author/Torisawa">Kentaro Torisawa</a> </span><em>Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language Technologies, </em>(<em>2006</em>)Mon Nov 06 21:01:19 CET 2006Proc. of the ISWC2006 Workshop on Web Content Mining with Human Language TechnologiesConstructing Dictionaries for Named Entity Recognition on Specific Domains from the Web2006WCMHLT2006 linguistics http://www.bibsonomy.org/bibtex/24b3336e01652540bfe38efbeeff26618/lysander07lysander072006-11-06T20:36:31+01:00WCMHLT2006 desaster emergency decisionsupport <span class="authorEditorList"><a href="/author/Chapman">Sam Chapman</a>, and <a href="/author/Ciravegna">Fabio Ciravegna</a> </span><em>Proc. of ISWC2006 Workshop on Web Content Mining with Human Language Technology, </em>(<em>2006</em>)Mon Nov 06 20:36:31 CET 2006Proc. of ISWC2006 Workshop on Web Content Mining with Human Language TechnologyFocused Data Mining for decision support in Emergency Response Scenarios.2006WCMHLT2006 desaster emergency decisionsupport http://www.bibsonomy.org/bibtex/2574c228e27d09c99971b83a6a64ce5a3/lysander07lysander072006-11-06T20:11:04+01:00ontologyLearning WCMHLT2006 <span class="authorEditorList"><a href="/author/Weber">Nicolas Weber</a>, and <a href="/author/Buitelaar">Paul Buitelaar</a> </span><em>Proc. of ISWC2006 Workshop on Web Content Mining with Human Language Technologies, </em>(<em>2006</em>)Mon Nov 06 20:11:04 CET 2006Proc. of ISWC2006 Workshop on Web Content Mining with Human Language TechnologiesWeb-based Ontology Learning with ISOLDE2006ontologyLearning WCMHLT2006 http://www.bibsonomy.org/bibtex/2b4551ac88a3954153d61e86be6f188c2/lysander07lysander072006-11-06T20:01:03+01:00ontologyLearning WCMHLT2006 linguistics ontologies wikipedia <span class="authorEditorList"><a href="/author/Herbelot">Aurelie Herbelot</a>, and <a href="/author/Copestake">Ann Copestake</a> </span><em>Proc.of the ISWC 2006 Workshop on Web Content Mining with Human Language Technologies, </em>(<em>2006</em>)Mon Nov 06 20:01:03 CET 2006Proc.of the ISWC 2006 Workshop on Web Content Mining with Human Language TechnologiesAcquiring Ontological Relationships from Wikipedia Using RMRS2006ontologyLearning WCMHLT2006 linguistics ontologies wikipedia We investigate the extraction of ontologies from biological text using a semantic representation derived from a robust parser. The use of a semantic representation avoids the problems that traditional pattern-based approaches have with complex syntactic constructions and long-distance dependencies. The discovery of taxonomic relationships is explored in a corpus consisting of 12,200 animal-related articles from the online encyclopaedia Wikipedia. The semantic representation used is Robust Minimal Recursion Semantics (RMRS). Initial experiments show good results in systematising extraction across a variety of hyponymic constructions.- Ontology extraction - Using wikipedia as text corpus - Semantics represented by RMRS