Word prediction, or language modeling, is the task of predicting the most likely words following the preceding text. It has many applications, such as suggesting the next word as text is entered, as an aid in resolving ambiguity in speech and handwriting recognition, and in machine translation.

• Implement a recurrent neural network (RNN) language model
• Implement an n-gram predictor as a baseline model
• Compare accuracy of the two methods for different training set sizes
• Compare time and space complexity and usage of the two algorithms
• Generate some text using both models for qualitative comparison

The final report is available here (PDF).

The project will work on a default Amazon EC2 AMI distribution if you'd like a clean slate - the base Python used is the Anaconda3 4.2.0 distribution with Python 3.5.2.

• Select the micro instance (free tier) and launch with a key pair.
• Login with username ec2-user
• Download Anaconda3 - wget https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh
• Make executable - chmod +x Anaconda3-4.2.0-Linux-x86_64.sh
• Run installer - ./Anaconda3-4.2.0-Linux-x86_64.sh
• Add path - source .bashrc
• Test it - ipython
• Install git - sudo yum install git

Once you have a Python 3.5 system set up,

• Clone this repository - git clone https://github.com/goldservice2017/Word-Prediction-with-RNN.git
• CD into the project - cd Word-Prediction-with-RNN
• Install Python packages - pip install -r requirements.txt
• Run make download to download and unzip the GloVe word vectors (~5 minutes)
• Run make rnn or make ngram to train and test the different models (the RNN is set up to train on 1% of the data for evaluation purposes)
• Run make plots to show a plot of accuracy vs train amount for ngram and RNN