Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling

  title={Distributional Representations for Handling Sparsity in Supervised Sequence-Labeling},
  author={Fei Huang and Alexander Yates},
Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently, they have difficulty estimating parameters for types which appear in the test set, but seldom (or never) appear in the training set. We demonstrate that distributional representations of word types, trained on unannotated text, can be used to improve performance on rare words. We incorporate aspects of these… CONTINUE READING
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  • In an experiment on a standard chunking dataset, our best technique improves a chunker from 0.76 F1 to 0.86 F1 on chunks beginning with rare words.


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