Difference between revisions of "Finkel and Manning, EMNLP 2009. Nested Named Entity Recognition"

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The authors model each sentence as a constituent tree. Each named entity would correspond to a phrase in the tree (i.e a subtree). A root node would connect the entire sentence. In addition, the POS tags of non-entities are also modeled. The diagram above is one such example of a "named entity tree".  
 
The authors model each sentence as a constituent tree. Each named entity would correspond to a phrase in the tree (i.e a subtree). A root node would connect the entire sentence. In addition, the POS tags of non-entities are also modeled. The diagram above is one such example of a "named entity tree".  
  
[[Image:annoated.png]]
+
[[Image:annotated.png]]
  
 
The trees are first annotated and binarized (in a right branching manner) with parent and grandparent labels.
 
The trees are first annotated and binarized (in a right branching manner) with parent and grandparent labels.

Revision as of 20:00, 24 September 2011

Nested Named Entity Recognition, by J. R Finkel, C. D Manning. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009.

This Paper is available online [1].

Under construction 09/24

Summary

This paper focuses on a variant of the Named Entity Recognition problem. They present a method for identifying nested named entities using a discriminative constituency parser.

Nested ne.png

An example of a nested named entity in the first 3 tokens of the example sentence, which standard "flat" NER systems are unable to distinguish.

Brief description of the method

The authors model each sentence as a constituent tree. Each named entity would correspond to a phrase in the tree (i.e a subtree). A root node would connect the entire sentence. In addition, the POS tags of non-entities are also modeled. The diagram above is one such example of a "named entity tree".

Annotated.png

The trees are first annotated and binarized (in a right branching manner) with parent and grandparent labels.

Experimental Result

Dataset

The authors have released TwitterNER dataset and source code for the paper. The demo and data are available online at [2].

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