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Hierarchical Name Entity Recognition

Traditional NER assumes each entity type as an independent class. However, entities can have a hierarchical structure. Genia Corpus Models are based on MEMM classifier, offering Model 1 with ancestor types as features, Model 2 with a classifier at each level, and Model 3 with local and global weights in a tree structure for classification. Acknowledgements to David, Mihai, instructors, and TAs for their contributions.

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Hierarchical Name Entity Recognition

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  1. Hierarchical Name Entity Recognition

  2. Motivation • Traditional NER assume that each entity type is an independent class. • However, they can have a hierarchical structure

  3. Genia Corpus

  4. Models • All models are based on MEMM classifier • Model 1 • Just take the ancestor types to be features • Model 2 • Train a classifier at each level • Do verterbi on paths in the tree

  5. Model 3 • Every node in the tree has a local weight and global weight • Global weight is for classification. • The sum of the local weights from the root to the node • Example

  6. Results

  7. Acknowledgement • Thanks David and Mihai for insightful discussions • Thanks instructors for excellent courses • Thanks TAs for hard work.

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