Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
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
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Kernel methods for relation extraction
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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We present an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. It works by calculating eigen-vectors of an adjacency graph's Laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. This method can address two difficulties encoutered in Hasegawa et al. (2004)'s hierarchical clustering: no consideration of manifold structure in data, and requirement to provide cluster number by users. Experiment results on ACE corpora show that this spectral clustering based approach outperforms Hasegawa et al. (2004)'s hierarchical clustering method and a plain k-means clustering method.