ML Anthology

Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution

Cosmin Bejan, Matthew Titsworth, Andrew Hickl, Sanda Harabagiu
NeurIPS 2009 pp. 73-81
/neurips/2009/bejan2009neurips-nonparametric/

Abstract

We present a sequence of unsupervised, nonparametric Bayesian models for clustering complex linguistic objects. In this approach, we consider a potentially infinite number of features and categorical outcomes. We evaluate these models for the task of within- and cross-document event coreference on two corpora. All the models we investigated show significant improvements when compared against an existing baseline for this task.

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Cite

Text

Bejan et al. "Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution." Neural Information Processing Systems, 2009.

Markdown

[Bejan et al. "Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution." Neural Information Processing Systems, 2009.](https://mlanthology.org/neurips/2009/bejan2009neurips-nonparametric/)

BibTeX

@inproceedings{bejan2009neurips-nonparametric,
  title     = {{Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution}},
  author    = {Bejan, Cosmin and Titsworth, Matthew and Hickl, Andrew and Harabagiu, Sanda},
  booktitle = {Neural Information Processing Systems},
  year      = {2009},
  pages     = {73-81},
  url       = {https://mlanthology.org/neurips/2009/bejan2009neurips-nonparametric/}
}