Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Kernel methods for relation extraction
The Journal of Machine Learning Research
An annotation scheme for free word order languages
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
Co-ranking Authors and Documents in a Heterogeneous Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
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Relation extraction from texts is a research topic since the message understanding conferences. Most investigations dealt with English texts. However, the heuristics found for these do not perform well when applied to a language with free word order, as is, e.g., German. In this paper, we present a German annotated corpus for relation extraction. We have implemented the state of the art methods of relation extraction using kernel methods and evaluate them on this corpus. The poor results led to a feature set which focusses on all words of the sentence and a tree kernel which includes words, in addition to the syntactic structure. The relation extraction is applied to monitoring a graph of economic company-directors network.