Representation learning for words and Labelled Documents by modelling them as graph (part of IRE Project at IIIT-H)
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word2graph2vec
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README.md

README.md

word2graph2vec

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.

Results

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

Authors

For any queries related to project you can contact the authors:

Shashank Gupta - 27392shashankgupta@gmail.com

Karan Chandnani - karanchandnani21@gmail.com

Nishant Prateek - nishant.prateek@research.iiit.ac.in