Induction of semantic classes from natural language text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Algorithms
Unsupervised named entity recognition using syntactic and semantic contextual evidence
Computational Linguistics
Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Acquisition of categorized named entities for web search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Named entity discovery using comparable news articles
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
SIGNUM: a graph algorithm for terminology extraction
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Multi-modality in one-class classification
Proceedings of the 19th international conference on World wide web
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We propose a system which builds, in a semi-supervised manner, a resource that aims at helping a NER system to annotate corpus-specific named entities. This system is based on a distributional approach which uses syntactic dependencies for measuring similarities between named entities. The specificity of the presented method however, is to combine a clique-based approach and a clustering technique that amounts to a soft clustering method. Our experiments show that the resource constructed by using this clique-based clustering system allows to improve different NER systems.