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High-order co-clustering text data on semantics-based representation model
Abstract The language modeling approach is widely uThe language modeling approach is widely used to improve the performance of text mining in recent years because of its solid theoretical foundation and empirical effectiveness. In essence, this approach centers on the issue of estimating an accurate model by choosing appropriate language models as well as smooth techniques. Semantic smoothing, which incorporates semantic and contextual information into the language models, is effective and potentially significant to improve the performance of text mining. In this paper, we proposed a high-order structure to represent text data by incorporating background knowledge, Wikipedia. The proposed structure consists of three types of objects, term, document and concept. Moreover, we firstly combined the high-order co-clustering algorithm with the proposed model to simultaneously cluster documents, terms and concepts. Experimental results on benchmark data sets (20Newsgroups and Reuters-21578) have shown that our proposed high-order co-clustering on high-order structure outperforms the general co-clustering algorithm on bipartite text data, such as document-term, document-concept and document-(term+concept).ument-concept and document-(term+concept).
Abstractsub The language modeling approach is widely uThe language modeling approach is widely used to improve the performance of text mining in recent years because of its solid theoretical foundation and empirical effectiveness. In essence, this approach centers on the issue of estimating an accurate model by choosing appropriate language models as well as smooth techniques. Semantic smoothing, which incorporates semantic and contextual information into the language models, is effective and potentially significant to improve the performance of text mining. In this paper, we proposed a high-order structure to represent text data by incorporating background knowledge, Wikipedia. The proposed structure consists of three types of objects, term, document and concept. Moreover, we firstly combined the high-order co-clustering algorithm with the proposed model to simultaneously cluster documents, terms and concepts. Experimental results on benchmark data sets (20Newsgroups and Reuters-21578) have shown that our proposed high-order co-clustering on high-order structure outperforms the general co-clustering algorithm on bipartite text data, such as document-term, document-concept and document-(term+concept).ument-concept and document-(term+concept).
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-20841-6-15  +
Has author Liping Jing + , Jiali Yun + , Jian Yu + , Jiao-Sheng Huang +
Has extra keyword Background knowledge + , Benchmark data + , Cluster documents + , Co-clustering + , Contextual information + , High-order + , High-order structure + , Language model + , Language modeling + , Representation Model + , Reuters-21578 + , Text data + , Text mining + , Theoretical foundations + , Wikipedia + , Computational linguistics + , Data mining + , Natural language processing systems + , Semantics + , Clustering algorithms +
Has keyword High-order co-clustering + , Representation Model + , Semantics + , Text mining + , Wikipedia +
Isbn 9783642208409  +
Issue PART 1  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 171–182  +
Published in Lecture Notes in Computer Science +
Title High-order co-clustering text data on semantics-based representation model +
Type conference paper  +
Volume 6634 LNAI  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 7 November 2014 19:05:09  +
Categories 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. 7 November 2014 19:05:09  +
DateThis property is a special property in this wiki. 2011  +
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