The nature of statistical learning theory
The nature of statistical learning theory
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting Hierarchy in Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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
Kernel methods for relation extraction
The Journal of Machine Learning Research
Natural language question answering: the view from here
Natural Language Engineering
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Modeling commonality among related classes in relation extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Hierarchical learning strategy in relation extraction using support vector machines
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Multi-task transfer learning for weakly-supervised relation extraction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
Exploiting background knowledge for relation extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A combination of topic models with max-margin learning for relation detection
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
| Hi-index | 0.00 |
This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in semantic relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding discriminative function can be determined more reliably and guide the discriminative function learning in the lower-level one more effectively, which otherwise might suffer from limited training data. In this paper, two classifier learning approaches, i.e. the simple perceptron algorithm and the state-of-the-art Support Vector Machines, are applied using the hierarchical learning strategy. Moreover, several kinds of class hierarchies either manually predefined or automatically clustered are explored and compared. Evaluation on the ACE RDC 2003 and 2004 corpora shows that the hierarchical learning strategy much improves the performance on least- and medium-frequent relations.