Stanfordnlp

This is a maintenance release of stanfordnlp. This release features:

* Allowing the tokenizer to treat the incoming document as pretokenized with space separated words in newline separated sentences. Set `tokenize_pretokenized` to `True` when building the pipeline to skip the neural tokenizer, and run all downstream components with your own tokenized text. (24, 34)
* Speedup in the POS/Feats tagger in evaluation (up to 2 orders of magnitude). (18)
* Various minor fixes and documentation improvements

We would also like to thank the following community members for their contribution:
Code improvements: lwolfsonkin
Documentation improvements: 0xflotus
And thanks to everyone that raised issues and helped improve stanfordnlp!

The initial release of StanfordNLP. StanfordNLP is the combination of the software package used by the Stanford team in the CoNLL 2018 Shared Task on Universal Dependency Parsing, and the group’s official Python interface to the [Stanford CoreNLP software](https://stanfordnlp.github.io/CoreNLP). This package is built with highly accurate neural network components that enables efficient training and evaluation with your own annotated data. The modules are built on top of [PyTorch](https://pytorch.org/) (v1.0.0).

StanfordNLP features:

- Native Python implementation requiring minimal efforts to set up;
- Full neural network pipeline for robust text analytics, including tokenization, multi-word token (MWT) expansion, lemmatization, part-of-speech (POS) and morphological features tagging and dependency parsing;
- Pretrained neural models supporting 53 (human) languages featured in 73 treebanks;
- A stable, officially maintained Python interface to CoreNLP.