Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs

@article{Ballesteros2015ImprovedTP,
  title={Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs},
  author={Miguel Ballesteros and Chris Dyer and Noah A. Smith},
  journal={ArXiv},
  year={2015},
  volume={abs/1508.00657}
}
We present extensions to a continuous-state dependency parsing method that makes it applicable to morphologically rich languages. Starting with a high-performance transition-based parser that uses long short-term memory (LSTM) recurrent neural networks to learn representations of the parser state, we replace lookup-based word representations with representations constructed from the orthographic representations of the words, also using LSTMs. This allows statistical sharing across word forms… CONTINUE READING

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