skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Katiyar, Arzoo"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ( , Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)
    We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-the-art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention 
    more » « less
    Full Text Available