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Building semantic kernels for text classification using Wikipedia
Abstract Document classification presents difficultDocument classification presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. The traditional document representation is a word-based vector (Bag of Words, or BOW), where each dimension is associated with a term of the dictionary containing all the words that appear in the corpus. Although simple and commonly used, this representation has several limitations. It is essential to embed semantic information and conceptual patterns in order to enhance the prediction capabilities of classification algorithms. In this paper, we overcome the shortages of the BOW approach by embedding background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the representation of documents. Our empirical evaluation with real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BOW technique, and to other recently developed methods., and to other recently developed methods.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Conclusion To the best of our knowledge, this paper rTo the best of our knowledge, this paper represents a first attempt to improve text classification by defining concept-based kernels using Wikipedia. Our approach overcomes the limitations of the bag-of-words approach by incorporating background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the content of documents. This methodology is able to keep multi-word concepts unbroken, it captures the semantic closeness to synonyms, and performs word sense disambiguation for polysemous terms. We note that our approach to highlight the semantic content of documents, from the definition of a proximity matrix, to the disambiguation of terms and to the identification of eligible candidate concepts, is totally unsupervised, i.e. makes no use of the class labels associated to documents. Thus, the same enrichment procedure could be extended to enhance the clustering of documents, when indeed class labels are not available, or too expensive to obtain. On the other hand, for classification problems where class labels are available, one could use them to facilitate the disambiguation process, and the identification of crucial concepts in a document.ication of crucial concepts in a document.
Conference location New York, USA +
Data source Experiment responses  + , Archival records  + , Wikipedia pages  +
Doi 10.1145/1401890.1401976 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Building%2Bsemantic%2Bkernels%2Bfor%2Btext%2Bclassification%2Busing%2BWikipedia%22  +
Has author Pu Wang + , Carlotta Domeniconi +
Has domain Computer science +
Has topic Text classification +
Pages 713-721  +
Peer reviewed Yes  +
Publication type Conference paper  +
Published in International Conference on Knowledge Discovery and Data Mining +
Publisher Association forComputing Machinery +
Research design Experiment  +
Research questions In this paper, we overcome the shortages oIn this paper, we overcome the shortages of the BOW approach by embedding background knowledge derived from Wikipedia into a semantic kernel, which is then used to enrich the representation of documents. Our empirical evaluation with real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BOW technique, and to other recently developed methods., and to other recently developed methods.
Revid 10,689  +
Theories Undetermined
Theory type Design and action  +
Title Building semantic kernels for text classification using Wikipedia
Unit of analysis Article  +
Url http://dl.acm.org/citation.cfm?id=1401976  +
Wikipedia coverage Main topic  +
Wikipedia data extraction Dump  +
Wikipedia language Not specified  +
Wikipedia page type Article  +
Year 2008  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:24:27  +
Categories Text classification  + , Computer science  + , Publications with missing comments  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:21:13  +
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