Extraction de LExique par Variation d'Entropie - Lexicon extraction based on the variation of entropy
What is ELeVE ?
ELeVE is a library intended for computing an “autonomy estimate” score for substrings (all n-grams) in a corpus of text.
The autonomy score is based on normalised variation of branching entropies (nVBE) of strings, See [MagistrySagot2012] for a definiton of these terms
It was developed mainly for unsupervised segmentation of mandarin Chinese, but is language independant and was successfully used in 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"]) 2.0369977951049805 >>> storage.query_autonomy(["like", "potatoes"]) -0.3227022886276245
Eleve also store n-gram’s occurence count:
>>> storage.query_count(["New", "York"]) 2 >>> storage.query_count(["New", "potatoes"]) 0 >>> storage.query_count(["I", "like", "potatoes"]) 1 >>> storage.query_count(["potatoes"]) 2
Then, you can use it for segmentation, using an algorithm that look for the solution which maximize nVBE of resulting words:
>>> 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 python3-dev 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|
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