Extraction de LExique par Variation d'Entropie - Lexicon extraction based on the variation of entropy
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 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"]) 2.0369977951049805 >>> storage.query_autonomy(["like", "potatoes"]) -0.3227022886276245
Eleve also store n-gram’s frequency:
>>> storage.query_count(["New", "York"]) 2 >>> storage.query_count(["New", "potatoes"]) 0 >>> storage.query_count(["I", "like", "potatoes"]) 1 >>> storage.query_count(["potatoes"]) 2
The 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 dependancies. 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 welcomed !
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 word tanks to 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|