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Matches in DBpedia 2016-04 for { <http://wikidata.dbpedia.org/resource/Q48522> ?p ?o }

• Q48522 subject Q7211395.
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• Q48522 subject Q7841472.
• Q48522 subject Q8728647.
• Q48522 subject Q9149760.
• Q48522 abstract "In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing and ontology. WSD is identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings. The solution to this problem impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference et cetera.The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.To date, a rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.Current accuracy is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90%, with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in recent evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.".
• Q48522 wikiPageExternalLink EMNLP_2010_Navigli_Crisafulli.pdf.
• Q48522 wikiPageExternalLink senseval2.
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• Q48522 wikiPageExternalLink W02-0808.pdf.
• Q48522 wikiPageExternalLink snyder.ps.
• Q48522 wikiPageExternalLink P10-1154.pdf.
• Q48522 wikiPageExternalLink W04-2807.pdf.
• Q48522 wikiPageExternalLink babelfy.org.
• Q48522 wikiPageExternalLink babelnet.org.
• Q48522 wikiPageExternalLink naacl07.pdf.
• Q48522 wikiPageExternalLink ukb.
• Q48522 wikiPageExternalLink semcor3.0.tar.gz.
• Q48522 wikiPageExternalLink LkbTop.
• Q48522 wikiPageExternalLink semeval.
• Q48522 wikiPageExternalLink fsnlp.
• Q48522 wikiPageExternalLink senserelate.sourceforge.net.
• Q48522 wikiPageExternalLink word-sense-disambiguation-wsd.html.
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• Q48522 wikiPageExternalLink WSDTutorial.html.
• Q48522 wikiPageExternalLink ACM_Survey_2009_Navigli.pdf.
• Q48522 wikiPageExternalLink LRE_2009_McCarthy_Navigli.pdf.
• Q48522 wikiPageExternalLink PAMI_2005_Navigli_Velardi.pdf.
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• Q48522 wikiPageExternalLink www.senseval.org.
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• Q48522 wikiPageExternalLink wordsenses_emnlp07.pdf.
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