Usually, the terms used by users during tagging are different from the terms/queries used when searching for web resources, this problem is known as the "gulf of execution”, which describes the cognitive gap between a user’s goals and a system’s functionality or content. In this sense, this project tries to deal the above problem.

This is a project developed within the research group on telematics engineering at the University of Cauca.

##Requirements

• Python >= 2.6 or >= 3.3
• NLTK
• TextBlob
• Gensim

###1. Natural Language Toolkit (NLTK) nltk.org

NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets and tutorials supporting research and development in Natural Language Processing. For documentation, please visit http://nltk.org/

To install NLTK, run setup.py from an administrator account, e.g.:

sudo python setup.py install

For full installation instructions, please see http://nltk.github.com/install.html

###2. TextBlob: Simplified Text Processing

TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. Homepage: https://textblob.readthedocs.org/

####Get it now

$ pip install -U textblob
$ curl https://raw.github.com/sloria/TextBlob/master/download_corpora.py | python

Full documentation is available at https://textblob.readthedocs.org/.

###3. gensim -- Python Framework for Topic Modelling

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

For a Python3 port of gensim by Parikshit Samant, visit "this fork".

####Installation

This software depends on "NumPy" and "Scipy", two Python packages for scientific computing. You must have them installed prior to installing gensim.

The simple way to install gensim is::

sudo easy_install gensim

Or, if you have instead downloaded and unzipped the source tar.gz package, you'll need to run:

python setup.py test
sudo python setup.py install

For alternative modes of installation (without root privileges, development installation, optional install features), see the documentation.

This version has been tested under Python 2.7, and should run on any 2.5 <= Python < 3.0.