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

EMNLP 2015 Miguel BallesterosChris DyerNoah A. Smith

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... (read more)

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