Making large-scale support vector machine learning practical
Advances in kernel methods
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
More accurate tests for the statistical significance of result differences
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Enhanced hierarchical classification via isotonic smoothing
Proceedings of the 17th international conference on World Wide Web
Deep classification in large-scale text hierarchies
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Refined experts: improving classification in large taxonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Multilabel classification with meta-level features
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Modeling topic dependencies in hierarchical text categorization
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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In this paper, we model learning to rank algorithms based on structural dependencies in hierarchical multi-label text categorization (TC). Our method uses the classification probability of the binary classifiers of a standard top-down approach to generate k-best hypotheses. The latter are generated according to their global probability while at the same time satisfy the structural constraints between father and children nodes. The rank is then refined using Support Vector Machines and tree kernels applied to a structural representation of hypotheses, i.e., a hierarchy tree in which the outcome of binary one-vs-all classifiers is directly marked in its nodes. Our extensive experiments on the whole Reuters Corpus Volume 1 show that our models significantly improve over the state of the art in TC, thanks to the use of structural dependecies.