Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a nunsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage...

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Vydáno v:	Behavior research methods Ročník 38; číslo 2; s. 262 - 279
Hlavní autoři:	Smith, Andrew E., Humphreys, Michael S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Austin, TX Psychonomic Society 01.05.2006 Psychonomic Society, Inc
Témata:	Algorithms Automation Biological and medical sciences Collocations Comorbidity Computer Applications Computer Generated Language Analysis Concept mapping Content analysis Equipment Design Extraction Fundamental and applied biological sciences. Psychology Human subjects Humans Information retrieval Language Learning Lexical Semantics Linguistics Linguistics - instrumentation Machine Learning Mapping Miscellaneous Natural language Natural Language Processing Psychology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Semantic Relations Semantics Statistical Analysis Concept Human Evaluation Semantics Non linear model Cognition Natural language Software Text Dynamic model Algorithm
ISSN:	1554-351X, 1554-3528
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Abstract	The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a nunsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.
AbstractList	The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction - semantic and relational - using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks. Adapted from the source document The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a nunsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a nunsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks. The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks. [PUBLICATION ABSTRACT] The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks. The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a nunsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.
Author	Humphreys, Michael S. Smith, Andrew E.
Author_xml	– sequence: 1 givenname: Andrew E. surname: Smith fullname: Smith, Andrew E. – sequence: 2 givenname: Michael S. surname: Humphreys fullname: Humphreys, Michael S.
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18045068$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/16956103$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	2006 INIST-CNRS Copyright Psychonomic Society, Inc. May 2006
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References_xml	– start-page: Demo23 volume-title: HLT-NAACL 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: Companion volume year: 2003 ident: BF03192778_CR26 – volume-title: Text and corpus analysis: Computer-assisted studies of language and culture year: 1996 ident: BF03192778_CR28 – volume: 29 start-page: 42 year: 2003 ident: BF03192778_CR19 publication-title: Journal of Experimental Psychology: Learning, Memory, & Cognition doi: 10.1037/0278-7393.29.1.42 – volume: 84 start-page: 231 year: 1977 ident: BF03192778_CR20 publication-title: Psychological Review doi: 10.1037/0033-295X.84.3.231 – volume-title: Automatic text processing: The transformation, analysis, and retrieval of information by computer year: 1989 ident: BF03192778_CR23 – volume-title: Proceedings of the Fifth Australasian Document Computing Symposium year: 2000 ident: BF03192778_CR25 – ident: BF03192778_CR21 – volume-title: Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting year: 2002 ident: BF03192778_CR10 – volume-title: Marine corps doctrinal publications: Capstone publications year: 1997 ident: BF03192778_CR29 – ident: BF03192778_CR17 – volume-title: Collier’s encyclopedia year: 1992 ident: BF03192778_CR2 – volume-title: Human processes in intelligence analysis: Phase I overview year: 1979 ident: BF03192778_CR12 – volume-title: Knowledge representation: Logical, philosophical, and computational foundations year: 2000 ident: BF03192778_CR27 – ident: BF03192778_CR11 – volume-title: Content analysis: An introduction to its methodology year: 2004 ident: BF03192778_CR13 – volume: 25 start-page: 259 year: 1998 ident: BF03192778_CR15 publication-title: Discourse Processes doi: 10.1080/01638539809545028 – volume: 12 start-page: 177 year: 1997 ident: BF03192778_CR5 publication-title: Language & Cognitive Processes doi: 10.1080/016909697386844 – ident: BF03192778_CR9 – start-page: 373 volume-title: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics year: 1997 ident: BF03192778_CR4 doi: 10.3115/979617.979665 – volume-title: The measurement of meaning year: 1957 ident: BF03192778_CR22 – volume-title: Basic content analysis year: 1990 ident: BF03192778_CR30 doi: 10.4135/9781412983488 – start-page: 330 volume-title: Proceedings of the 15th Annual ACM SIGIR Conference on Research and Development in Information Retrieval year: 1992 ident: BF03192778_CR6 doi: 10.1145/133160.133215 – ident: BF03192778_CR24 – volume: 104 start-page: 211 year: 1997 ident: BF03192778_CR14 publication-title: Psychological Review doi: 10.1037/0033-295X.104.2.211 – start-page: 23 volume-title: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval year: 1994 ident: BF03192778_CR1 – volume-title: Clausewitz in English: The reception of Clausewitz in Britain and America, 1815–1945 year: 1994 ident: BF03192778_CR3 – start-page: 148 volume-title: CIKM ’98: Proceedings of the 7th International Conference on Information and Knowledge Management year: 1998 ident: BF03192778_CR8 doi: 10.1145/288627.288651 – ident: BF03192778_CR18 – start-page: 189 volume-title: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95) year: 1995 ident: BF03192778_CR31 doi: 10.3115/981658.981684 – volume-title: On war year: 1873 ident: BF03192778_CR7 – volume: 17 start-page: 231 year: 2004 ident: BF03192778_CR16 publication-title: International Journal of Intelligence & Counterintelligence doi: 10.1080/08850600490274908
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Snippet	The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in a... The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an...
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SubjectTerms	Algorithms Automation Biological and medical sciences Collocations Comorbidity Computer Applications Computer Generated Language Analysis Concept mapping Content analysis Equipment Design Extraction Fundamental and applied biological sciences. Psychology Human subjects Humans Information retrieval Language Learning Lexical Semantics Linguistics Linguistics - instrumentation Machine Learning Mapping Miscellaneous Natural language Natural Language Processing Psychology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Semantic Relations Semantics Statistical Analysis
Title	Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping
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