Representation learning for words and Labelled Documents by jointly representing words, labels and documents as nodes of a graph and then learning low dimensional representation of it.
The method is based on the paper by Tang J. et. al (http://research.microsoft.com/pubs/255567/fp292-Tang.pdf).
Current state of project
Current task is sentiment analysis on IMDB dataset with two labels (sentiments - positive, negative). It constructs word-word, word-doc and word-label graph from data and trains a skip-gram based model for learning low dimensional representation of nodes in graph.
Currently it trains model only on word-word graph. Training jointly on all three graphs is in development phase. We are running experiments.
Current implementaion has quite poor results. It gives ~55% accuracy on test.
State - Development
The project is still in development state. We will change the state of the project to alpha when it gives decent performance of dataset
For any queries related to project you can contact the authors:
Shashank Gupta - firstname.lastname@example.org
Karan Chandnani - email@example.com
Nishant Prateek - firstname.lastname@example.org