Contextual Information Extraction Technique based on 5 steps and using word embedding based models (CIET.5_embed) is a new context extraction technique based on vector space model. Analogous to the CIRT.5_embed, this technique assumes that the frequency relationship between terms is dependent, considering the reliance of a set of correlated terms (context) directly proportional to the frequency with his terms occurs in a text document.

This CIET.5_embed based script allows to convert a collection of text documents formed by terms into a collection of text documents formed by contexts. The main differences with other textual enrichment procedures such as named entity recognition and word sense disambiguation is that CIET.5_embed based contexts extracted considers the local influence of textual scopes, in addition to enabling the volume and quality of information in texts through external knowledge sources like Wikipedia.

Extracting a CIET.5_embed based set of contexts:

python3 CIET.5_embed.py --language EN --contexts 3 --thresholds 0.05 --model models/model --input in/db/ --output out/CIET.5_embed/txt/

• CIET.5_embed.py

These script expect a database folder following an specific hierarchy like shown below:

in/db/                 (main directory)
---> class_1/          (class_1's directory)
---------> file_1      (text file)
---------> file_2      (text file)
---------> ...
---> class_2/          (class_2's directory)
---------> file_1      (text file)
---------> file_2      (text file)
---------> ...
---> ...

Python 3 + PIP installation as super user:

apt-get install python3 python3-pip

Gensim installation as normal user:

pip3 install --upgrade gensim

NLTK + Scipy + Numpy installation as normal user:

pip3 install -U nltk scipy numpy

Project page on LABIC website: http://sites.labic.icmc.usp.br/MSc-Thesis_Antunes_2018