Generalized vector spaces model in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Approximate Dimension Equalization in Vector-based Information Retrieval
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machines Based on a Semantic Kernel for Text Categorization
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
MindNet: an automatically-created lexical resource
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Augmenting mobile localization with activities and common sense knowledge
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
Interactive information retrieval algorithm for wikipedia articles
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Most text categorization research exploit bag-of-words text representation. However, such representation makes it very hard to capture semantic similarity between text documents that share very little or even no vocabulary. In this paper we present preliminary results obtained with a novel approach that combines well established kernel text classifiers with external contextual commonsense knowledge. We propose a method for computing semantic similarity between words as a result of diffusion process in ConceptNet semantic space. Evaluation on a Reuters dataset show an improvement in precision of classification.