python-zpar 0.3

Introduction

python-zpar is a python wrapper around the ZPar parser. ZPar was written by Yue Zhang while he was at Oxford University. According to its home page: ZPar is a statistical natural language parser, which performs syntactic analysis tasks including word segmentation, part-of-speech tagging and parsing. ZPar supports multiple languages and multiple grammar formalisms. ZPar has been most heavily developed for Chinese and English, while it provides generic support for other languages. ZPar is fast, processing above 50 sentences per second using the standard Penn Teebank (Wall Street Journal) data.

I wrote python-zpar since I needed a fast and efficient parser for my NLP work which is primarily done in Python and not C++. I wanted to be able to use this parser directly from Python without having to create a bunch of files and running them through subprocesses. python-zpar not only provides a simply python wrapper but also provides an XML-RPC ZPar server to make batch-processing of large files easier.

python-zpar uses ctypes, a very cool foreign function library bundled with Python that allows calling functions in C DLLs or shared libraries directly.

Installation

Currently, python-zpar only works on 64-bit linux and OS X systems. Those are the two platforms I use everyday. I am happy to try to get python-zpar working on other platforms over time. Pull requests are welcome!

In order for python-zpar to work, it requires C functions that can be called directly. Since the only user-exposed entry point in ZPar is the command line client, I needed to write a shared library that would have functions built on top of the ZPar functionality but expose them in a way that ctypes could understand.

Therefore, in order to build python-zpar from scratch, we need to download the ZPar source, patch it with new functionality and compile the shared library. All of this happens automatically when you install with pip:

pip install python-zpar

If you are using conda, things are even faster since everything is pre-compiled:

conda install -c https://conda.binstar.org/desilinguist python-zpar

IMPORTANT: On OS X, the installation will only work with gcc installed using either macports or homebrew. The zpar source cannot be compiled with clang.

If you are curious about what the C functions in the shared library module look like, see src/zpar.lib.cpp.

Usage

To use python-zpar, you need the English models for ZPar. They can be downloaded from here. There are three models: a part-of-speech tagger, a constituency parser, and a dependency parser. For the purpose of the examples below, the models are in the english-models directory in the current directory.

Here’s a small example of how to use python-zpar:

from six import print_
from zpar import ZPar

# use the zpar wrapper as a context manager
with ZPar('english-models') as z:

    # get the parser and the dependency parser models
    tagger = z.get_tagger()
    depparser = z.get_depparser()

    # tag a sentence
    tagged_sent = tagger.tag_sentence("I am going to the market.")
    print_(tagged_sent)

    # get the dependency parse of the same sentence
    dep_parsed_sent = depparser.parse_sentence("I am going to the market.")
    print_(dep_parsed_sent)

The above code sample produces the following output:

I/PRP am/VBP going/VBG to/TO the/DT market/NN ./.

I       PRP   1    SUB
am      VBP   -1   ROOT
going   VBG   1    VC
to      TO    2    VMOD
the     DT    5    NMOD
market  NN    3    PMOD
.       .     1    P

Detailed usage with comments is shown in the included file examples/zpar_example.py. Run python zpar_example.py -h to see a list of all available options.

ZPar Server

The repository provides an python XML-RPC implementation of a ZPar server that makes it easier to process multiple sentences and files by loading the models just once (via the ctypes interface) and allowing clients to connect and request analyses. The implementation is in the file examples/zpar_server.py. The server is quite flexible and allows loading only the models that you need. Here’s an example of how to start the server with only the tagger and the dependency parser models loaded:

$> cd examples
$> python zpar_server.py --modeldir english-models --models tagger depparser
INFO:Initializing server ...
Loading tagger from english-models/tagger
Loading model... done.
Loading dependency parser from english-models/depparser
Loading scores... done. (14.9623s)
INFO:Registering introspection ...
INFO:Starting server on port 8859...

Run python zpar_server.py -h to see a list of all options.

Once the server is running, you can connect to it using a client. An example client is included in the file examples/zpar_client.py which can be run as follows:

$> cd examples
$> python zpar_client.py
INFO:Attempting connection to http://localhost:8859
INFO:Tagging "I am going to the market."
INFO:Output: I/PRP am/VBP going/VBG to/TO the/DT market/NN ./.
INFO:Dep Parsing "I am going to the market."
INFO:Output: I  PRP 1   SUB
am  VBP -1  ROOT
going   VBG 1   VC
to  TO  2   VMOD
the DT  5   NMOD
market  NN  3   PMOD
.   .   1   P

INFO:Tagging file test.txt into test.tag
INFO:Dep Parsing file test.txt into

Note that python-zpar and all of the example scripts should work with both Python 2.7 and Python 3.3. I have tested python-zpar on both Linux and Mac but not on Windows.

Node.js version

If you want to use ZPar in your node.js app, check out my other project node-zpar.

License

Although python-zpar is licensed under the MIT license - which means that you can do whatever you want with the wrapper code - ZPar itself is licensed under GPL v3.

ToDo

1. Improve error handling on both the python and C side.

2. Expose more functionality, e.g., Chinese word segmentation, parsing etc.

3. May be look into using CFFI instead of ctypes.