ISCA Archive Interspeech 2005
ISCA Archive Interspeech 2005

Linear models for structure prediction

Fernando C. N. Pereira

Over the last few years, several groups have been developing models and algorithms for learning to predict the structure of complex data, sequences in particular, that extend well-known linear classification models and algorithms, such as logistic regression, the perceptron algorithm, and support vector machines. These methods combine the advantages of discriminative learning with those of probabilistic generative models like HMMs and probabilistic context-free grammars. I will introduce linear models for structure prediction and their simplest learning algorithms, and exemplify their benefits with applications to text and speech processing, including information extraction, parsing, and language modeling.

doi: 10.21437/Interspeech.2005-2

Cite as: Pereira, F.C.N. (2005) Linear models for structure prediction. Proc. Interspeech 2005, 717-720, doi: 10.21437/Interspeech.2005-2

  author={Fernando C. N. Pereira},
  title={{Linear models for structure prediction}},
  booktitle={Proc. Interspeech 2005},