Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Elements of artificial neural networks
Elements of artificial neural networks
Map displays for information retrieval
Journal of the American Society for Information Science
Projections for efficient document clustering
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Self-organizing maps
SONIA: a service for organizing networked information autonomously
Proceedings of the third ACM conference on Digital libraries
On the merits of building categorization systems by supervised clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Document clustering for electronic meetings: an experimental comparison of two techniques
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
A semi-supervised document clustering technique for information organization
Proceedings of the ninth international conference on Information and knowledge management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Neural Network Agents for Learning Semantic Text Classification
Information Retrieval
Recurrent Neural Learning for Helpdesk Call Routing
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
CDM: an approach to learning in text categorization
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Data mining for hypertext: a tutorial survey
ACM SIGKDD Explorations Newsletter
On the quality of ART1 text clustering
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Selforganizing classification on the Reuters news corpus
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Self organization of a massive document collection
IEEE Transactions on Neural Networks
A note on self-organizing semantic maps
IEEE Transactions on Neural Networks
Web Page Clustering Using a Fuzzy Logic Based Representation and Self-Organizing Maps
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
Exploiting corpus-related ontologies for conceptualizing document corpora
Journal of the American Society for Information Science and Technology
Learning a taxonomy from a set of text documents
Applied Soft Computing
Concept chaining utilizing meronyms in text characterization
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Ontologies and terminologies: Continuum or dichotomy?
Applied Ontology - Ontologies and Terminologies: Continuum or Dichotomy?
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Three novel text vector representation approaches for neural network based document clustering are proposed. The first is the extended significance vector model (ESVM), the second is the hypernym significance vector model (HSVM) and the last is the hybrid vector space model (HyM). ESVM extracts the relationship between words and their preferred classified labels. HSVM exploits a semantic relationship from the WordNet ontology. A more general term, the hypernym, substitutes for terms with similar concepts. This hypernym semantic relationship supplements the neural model in document clustering. HyM is a combination of a TFxIDF vector and a hypernym significance vector, which combines the advantages and reduces the disadvantages from both unsupervised and supervised vector representation approaches. According to our experiments, the self-organising map (SOM) model based on the HyM text vector representation approach is able to improve classification accuracy and to reduce the average quantization error (AQE) on 10,000 full-text articles.