Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning question classifiers: the role of semantic information
Natural Language Engineering
Domain kernels for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Entity categorization over large document collections
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Instance-based ontology population exploiting named-entity substitution
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Weakly-supervised acquisition of labeled class instances using graph random walks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The SemEval-2007 WePS evaluation: establishing a benchmark for the web people search task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Domain kernels for text categorization
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Domain information for fine-grained person name categorization
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Assessing the challenge of fine-grained named entity recognition and classification
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Enhancing the open-domain classification of named entity using linked open data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Distributional models and lexical semantics in convolution kernels
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
A graph-based approach for ontology population with named entities
Proceedings of the 21st ACM international conference on Information and knowledge management
Entity discovery and annotation in tables
Proceedings of the 16th International Conference on Extending Database Technology
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We present a kernel-based approach for fine-grained classification of named entities. The only training data for our algorithm is a few manually annotated entities for each class. We defined kernel functions that implicitly map entities, represented by aggregating all contexts in which they occur, into a latent semantic space derived from Wikipedia. Our method achieves a significant improvement over the state of the art for the task of populating an ontology of people, although requiring considerably less training instances than previous approaches.