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Selective integration of background knowledge in TCBR systems
Abstract This paper explores how background knowledThis paper explores how background knowledge from freely available web resources can be utilised for Textual Case Based Reasoning. The work reported here extends the existing Explicit Semantic Analysis approach to representation, where textual content is represented using concepts with correspondence to Wikipedia articles. We present approaches to identify Wikipedia pages that are likely to contribute to the effectiveness of text classification tasks. We also study the effect of modelling semantic similarity between concepts (amounting to Wikipedia articles) empirically. We conclude with the observation that integrating background knowledge from resources like Wikipedia into TCBR tasks holds a lot of promise as it can improve system effectiveness even without elaborate manual knowledge engineering. Significant performance gains are obtained using a very small number of features that have very strong correspondence to how humans describe the domain.ondence to how humans describe the domain.
Abstractsub This paper explores how background knowledThis paper explores how background knowledge from freely available web resources can be utilised for Textual Case Based Reasoning. The work reported here extends the existing Explicit Semantic Analysis approach to representation, where textual content is represented using concepts with correspondence to Wikipedia articles. We present approaches to identify Wikipedia pages that are likely to contribute to the effectiveness of text classification tasks. We also study the effect of modelling semantic similarity between concepts (amounting to Wikipedia articles) empirically. We conclude with the observation that integrating background knowledge from resources like Wikipedia into TCBR tasks holds a lot of promise as it can improve system effectiveness even without elaborate manual knowledge engineering. Significant performance gains are obtained using a very small number of features that have very strong correspondence to how humans describe the domain.ondence to how humans describe the domain.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-23291-6_16  +
Has author Patelia A. + , Chakraborti S. + , Wiratunga N. +
Has extra keyword Background knowledge + , Explicit semantics + , Performance Gain + , Semantic similarity + , System effectiveness + , Text classification + , Textual content + , Web resources + , Wikipedia + , Case based reasoning + , Knowledge engineering + , Semantics + , World Wide Web +
Isbn 9783642232909  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 196–210  +
Published in Lecture Notes in Computer Science +
Title Selective integration of background knowledge in TCBR systems +
Type conference paper  +
Volume 6880 LNAI  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 8 November 2014 05:56:33  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 05:56:33  +
DateThis property is a special property in this wiki. 2011  +
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