ELeVE documentation

Welcome to ELeVE’s documentation. After the table of content that follows, you will find a documentation to help you getting started (instillation, basic usage, ...).

What is ELeVE ?

ELeVE is a library for calculating a specialized language model from a corpus of text.

It allows you to use statistics from the training corpus to calculate branching entropy, and autonomy measures for n-grams of text. See [MagistrySagot2012] for a definiton of these terms (autonomy is also called « nVBE » for « normalized entropy variation »)

It was mainly developed for segmentation of mandarin Chinese, but was successfully used to research on other tasks like keyphrase extraction.

Full documentation is available on http://pythonhosted.org/eleve/.

In a nutshell

Here is a simple “getting started”. First you have to train a model:

>>> from eleve import MemoryStorage
>>> storage = MemoryStorage()
>>> # Then the training itself:
>>> storage.add_sentence(["I", "like", "New", "York", "city"])
>>> storage.add_sentence(["I", "like", "potatoes"])
>>> storage.add_sentence(["potatoes", "are", "fine"])
>>> storage.add_sentence(["New", "York", "is", "a", "fine", "city"])

And then you cat query it:

>>> storage.query_autonomy(["New", "York"])
>>> storage.query_autonomy(["like", "potatoes"])

Eleve also store n-gram’s occurence count:

>>> storage.query_count(["New", "York"])
>>> storage.query_count(["New", "potatoes"])
>>> storage.query_count(["I", "like", "potatoes"])
>>> storage.query_count(["potatoes"])

Then, you can use it for segmentation:

>>> from eleve import Segmenter
>>> s = Segmenter(storage)
>>> # segment up to 4-grams, if we used the same storage as before.
>>> s.segment(["What", "do", "you", "know", "about", "New", "York"])
[['What'], ['do'], ['you'], ['know'], ['about'], ['New', 'York']]


You will need some dependencies. On Ubuntu:

$ sudo apt-get install libboost-python-dev libboost-filesystem-dev libleveldb-dev

Then to install eleve:

$ pip install eleve

or if you have a local clone of source folder:

$ python setup.py install

Get the source

Source are stored on github:

$ git clone https://github.com/kodexlab/eleve


Install the development environment:

$ git clone https://github.com/kodexlab/eleve
$ cd eleve
$ virtualenv ENV -p /usr/bin/python3
$ source ENV/bin/activate
$ pip install -r requirements.txt
$ pip install -r requirements.dev.txt

Pull requests are welcome!

To run tests:

$ make testall

To build the doc:

$ make doc

then open: docs/_build/html/index.html

Warning: You need to have eleve accesible in the python path to run tests (and to build doc). For that you can install eleve as a link in local virtualenv:

$ pip install -e .

(Note: this is indicated in pytest good practice )


If you use eleve for an academic publication, please cite this paper:

[MagistrySagot2012]Magistry, P., & Sagot, B. (2012, July). Unsupervized word segmentation: the case for mandarin chinese. In Proceedings of the 50th Annual Meeting of the ACL: Short Papers-Volume 2 (pp. 383-387). http://www.aclweb.org/anthology/P12-2075

Indices and tables