A concept-based model for enhancing text categorization
Most of text categorization techniques are based on word and/or phrase analysis of the text.
Statistical analysis of a term frequency captures the importance of the term within a …
Statistical analysis of a term frequency captures the importance of the term within a …
An efficient model for enhancing text categorization using sentence semantics
Most of text categorization techniques are based on word and/or phrase analysis of the text.
Statistical analysis of a term frequency captures the importance of the term within a …
Statistical analysis of a term frequency captures the importance of the term within a …
Use of a domain-specific ontology to support automated document categorization at the concept level: Method development and evaluation
Voluminous, conveniently accessible textual documents, created and disseminated by
modern information technology, makes automated document organization increasingly …
modern information technology, makes automated document organization increasingly …
Automatic text categorization based on content analysis with cognitive situation models
Y Guo, Z Shao, N Hua - Information Sciences, 2010 - Elsevier
Text categorization is an important research area of text mining. The original purpose of text
categorization is to recognize, understand and organize different types of texts or …
categorization is to recognize, understand and organize different types of texts or …
[PDF][PDF] Training-less ontology-based text categorization
M Janik, K Kochut - 2008 - getd.libs.uga.edu
In this dissertation, we present a novel approach for automatic text categorization that
combines some features of traditional text categorization approaches with the knowledge …
combines some features of traditional text categorization approaches with the knowledge …
Extract semantic information from wordnet to improve text classification performance
R Bai, X Wang, J Liao - … Conference on Advanced Computer Science and …, 2010 - Springer
Since a decade, text categorization has become an active field of research in the machine
learning community. Most of the approaches are based on the term occurrence frequency …
learning community. Most of the approaches are based on the term occurrence frequency …
An improved concept vector space model for ontology based classification
This paper proposes an improved concept vector space (ICVS) model which takes into
account the importance of ontology concepts. Concept importance shows how important a …
account the importance of ontology concepts. Concept importance shows how important a …
An efficient concept-based mining model for enhancing text clustering
Most of the common techniques in text mining are based on the statistical analysis of a term,
either word or phrase. Statistical analysis of a term frequency captures the importance of the …
either word or phrase. Statistical analysis of a term frequency captures the importance of the …
[PDF][PDF] Ontology-based concept weighting for text documents
HH Tar, TTS Nyunt - world Academy of Science, engineering and …, 2011 - academia.edu
Documents clustering become an essential technology with the popularity of the Internet.
Clustering has been very popular for a long time because it provides unique ways of …
Clustering has been very popular for a long time because it provides unique ways of …
[PDF][PDF] Improving text categorization by using a topic model
W Sriurai - Advanced Computing, 2011 - Citeseer
Most text categorization algorithms represent a document collection as a Bag of Words
(BOW). The BOW representation is unable to recognize synonyms from a given term set and …
(BOW). The BOW representation is unable to recognize synonyms from a given term set and …