Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
The feature quantity: an information theoretic perspective of Tfidf-like measures
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Machine Learning
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
Bayesian online classifiers for text classification and filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
NLP-driven IR: evaluating performances over a text classification task
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
International Journal of Metadata, Semantics and Ontologies
A New Fuzzy Hierarchical Classification Based on SVM for Text Categorization
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Hi-index | 0.00 |
The classification of documents is an interesting topic of recent terminological investigations, in particular the technological ones. Some sophisticated techniques have been developed which provide the classification based upon the recognition of specific linguistic features, such as specific terms or occurrences of phrases. A limited number of cases exist of real document classification applications that make use of natural language processing techniques providing both statistical analysis and human supervision, where the system fully automates the classification process, but the instruction of the taxonomy is a totally human centred activity. In this paper we focus on an application with the above mentioned features; we then introduce a methodology that makes use of this application. The fundamental argument in favour of a specific methodology is that the analysis which leads to the deployment of the term 'taxonomy' can be seen as an ontology construction: we also discuss this aspect as a general motivation.