Linear Co-occurrence Rate Networks (L-CRNs) for Sequence Labeling

@inproceedings{Zhu2014LinearCR,
  title={Linear Co-occurrence Rate Networks (L-CRNs) for Sequence Labeling},
  author={Zhemin Zhu and Djoerd Hiemstra and Peter M. G. Apers},
  booktitle={SLSP},
  year={2014}
}
Sequence labeling has wide applications in natural language processing and speech processing. Popular sequence labeling models suffer from some known problems. Hidden Markov models (HMMs) are generative models and they cannot encode transition features; Conditional Markov models (CMMs) suffer from the label bias problem; And training of conditional random fields (CRFs) can be expensive. In this paper, we propose Linear Co-occurrence Rate Networks (L-CRNs) for sequence labeling which avoid the… CONTINUE READING

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
2 Extracted Citations
21 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 21 references

On the effect of the label bias problem in part-of-speech tagging

  • P. Le-Hong, X. H. Phan, T. T. Tran
  • The 10th IEEE RIVF International Conference on…
  • 2013
2 Excerpts

Crf++: Yet another crf toolkit. free software (March 2012), http:// crfpp.googlecode.com/svn/trunk/doc/index.html

  • T. Kudo
  • 2012
1 Excerpt

Similar Papers

Loading similar papers…