SyntaxNet Toolkit

References

http://googleresearch.blogspot.tw/2016/05/announcing-syntaxnet-worlds-most.html
- QUOTE: SyntaxNet is a framework for what’s known in academic circles as a syntactic parser, which is a key first component in many NLU systems. Given a sentence as input, it tags each word with a part-of-speech (POS) tag that describes the word's syntactic function, and it determines the syntactic relationships between words in the sentence, represented in the dependency parse tree. These syntactic relationships are directly related to the underlying meaning of the sentence in question.

https://github.com/tensorflow/models/tree/master/syntaxnet
- QUOTE: ... Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text.
  So, how accurate is Parsey McParseface? For this release, we tried to balance a model that runs fast enough to be useful on a single machine (e.g. ~600 words/second on a modern desktop) and that is also the most accurate parser available. ... with SyntaxNet you can train larger networks with more hidden units and bigger beam sizes if you want to push the accuracy even further: Andor et al. (2016) is simply a SyntaxNet model with a larger beam and network. ...
  ... Parsey McParseface is also state-of-the-art for part-of-speech (POS) tagging (numbers below are per-token accuracy) ... ... SyntaxNet, an open-source neural network framework for TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you, and that you can use to analyze English text.