Part of Speech Tagging Demonstration

For best results, manual input before running Dracula.

Dracula is a deep, LSTM-based sequence tagger which operates on character embeddings. It can (so far) do binary polarity classification and part-of-speech tagging. Using characters means Dracula's models are small enough to run anywhere, including in the browser.

This demonstration uses GATE's bootstrapped training data and 32-size embeddings, with their mostly PTB tagset.

Tokenizer implementation by Myle Ott.

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