Hierarchical mixtures of experts and the EM algorithm
Neural Computation
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Self-organizing maps
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Clustering documents into a web directory for bootstrapping a supervised classification
Data & Knowledge Engineering - Special issue: WIDM 2003
Taxonomies by the numbers: building high-performance taxonomies
Proceedings of the 14th ACM international conference on Information and knowledge management
An integrated system for building enterprise taxonomies
Information Retrieval
Text classification from unlabeled documents with bootstrapping and feature projection techniques
Information Processing and Management: an International Journal
A hidden Markov model-based text classification of medical documents
Journal of Information Science
Improving text categorization bootstrapping via unsupervised learning
ACM Transactions on Speech and Language Processing (TSLP)
Labeling design documents based on operators' consensus-A case study of robotic design
Computers in Industry
Towards the taxonomy-oriented categorization of yellow pages queries
ACM Transactions on Internet Technology (TOIT)
Helping physicians to organize guidelines within conceptual hierarchies
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Category hierarchy maintenance: a data-driven approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Managing the hierarchical organization of data is starting to play a key role in the knowledge management community due to the great amount of human resources needed to create and maintain these organized repositories of information. Machine learning community has in part addressed this problem by developing hierarchical supervised classifiers that help maintainers to categorize new resources within given hierarchies. Although such learning models succeed in exploiting relational knowledge, they are highly demanding in terms of labeled examples, because the number of categories is related to the dimension of the corresponding hierarchy. Hence, the creation of new directories or the modification of existing ones require strong investments.This paper proposes a semi-automatic process (interleaved with human suggestions) whose aim is to minimize (simplify) the work required to the administrators when creating, modifying, and maintaining directories. Within this process, bootstrapping a taxonomy with examples represents a critical factor for the effective exploitation of any supervised learning model. For this reason we propose a method for the bootstrapping process that makes a first hypothesis of categorization for a set of unlabeled documents, with respect to a given empty hierarchy of concepts. Based on a revision of Self-Organizing Maps, namely TaxSOM, the proposed model performs an unsupervised classification, exploiting the a-priori knowledge encoded in a taxonomy structure both at the terminological and topological level. The ultimate goal of TaxSOM is to create the premise for successfully training a supervised classifier.