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
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SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
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IEEE Internet Computing
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Advances in Neural Information Processing Systems 5, [NIPS Conference]
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Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Semantic indexing using WordNet senses
RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11
Self organization of a massive document collection
IEEE Transactions on Neural Networks
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Neural Network Based Document Clustering Using WordNet Ontologies
International Journal of Hybrid Intelligent Systems
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ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
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Information Processing and Management: an International Journal
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AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Improved ROCK for text clustering using asymmetric proximity
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
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In this paper we propose an integration of a selforganizing map and semantic networks from WordNet for a text classification task using the new Reuters news corpus. This neural model is based on significance vectors and benefits from the presentation of document clusters. The Hypernym relation in WordNet supplements the neural model in classification. We also analyse the relationships of news headlines and their contents of the new Reuters corpus by a series of experiments. This hybrid approach of neural selforganization and symbolic hypernym relationships is successful to achieve good classification rates on 100,000 full-text news articles. These results demonstrate that this approach can scale up to a large real-world task and show a lot of potential for text classification.