ConText v4: Neural networks for text categorization
ConText v4.00 provides a C++ implementation of neural networks for text categorization described in:
ConText v4.00 is available at http://riejohnson.com/cnn_download.html.
System Requirements: This software runs only on a CUDA-capable GPU such as Tesla K20. That is, your system must have a GPU and an appropriate version of CUDA installed. The provided makefile
and example shell scripts are for Unix-like systems. Testing was done on Linux. In principle, the C++ code should compile and run also in other systems (e.g., Windows), but no guarantee. See README
for more details.
Download & Documentation: See http://riejohnson.com/cnn_download.html#download.
Getting Started
README
(not README.md
).make
, after customizing makefile
as needed.make
also decompresses sample text files that exceed GitHub file size limit
and does chmod +x
on shell scripts.)examples/
and enter ./sample.sh
.README
for installation trouble shooting.)examples/
. There is a table of the scripts in Section 1.6 of
User Guide.Data Source: The data files were derived from Large Move Review Dataset (IMDB) [MDPHN11] and Amazon reviews [ML13].
Licence: This program is free software issued under the GNU General Public License V3.
References
[MDPHN11] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. Learning word vectors for sentiment analysis. ACL 2011.
[ML13] Julian McAuley and Jure Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. RecSys 2013.
Note: This GitHub repository provides a snapshot of research code, which is constantly changing elsewhere for research purposes. For this reason, it is very likely that pull requests will be declined.