predictive text interface
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README.md

pt-voicebox

This is a writing interface intended to imitate the predictive text function on smartphones. It is not a bot! A user has to be involved.

Getting Started (Mac OS X)

Running the Program

  1. Open the Mac application called Terminal
  2. Enter cd ~/Desktop or wherever you would like to download the project (cd means "Change Directory" and ~/Desktop is a shortcut to your desktop)
  3. Enter git clone https://github.com/jbrew/pt-voicebox.git to download the project
  4. Enter cd pt-voicebox to go into the project directory
  5. Enter sudo easy_install pip (this will prompt you for your password)
  6. Enter pip install --user -r requirements.txt to download the project dependencies
  7. Enter bin/voicebox and follow the onscreen instructions

Adding Your Own Source Texts

  1. Enter cd ~/Desktop/pt-voicebox or wherever you downloaded the project
  2. Create a text file for each source text you want to use. Save them inside the texts folder within voicebox (pt-voicebox/texts)

Running the Tests

  1. Enter cd ~/Desktop/pt-voicebox or wherever you downloaded the project
  2. Enter nosetests tests

Project Structure

The classes are structured as follows:

  • A corpus has a tree with the frequencies of all n-grams up to a certain size present in a source, and information about which words precede and follow these
  • A voice is a weighted combination of corpora
  • A voicebox contains a list of voices, and has a user writing loop that allows for switching between them on the fly

Algorithm Overview

The approach to generating word lists is Markov-esque but is not strictly a Markov process, which would need to be stochastic. Here, the user has the final decision.

At each step of the sentence, the script uses the n most recent words to determine a list of the m most likely words to come next. The Markov determination of this list is a weighted combination of several lists, with higher weights given to lists of words that followed larger n-grams that constitute the immediate context.

For instance, when n=2 and the most recent two words in the sentence are "my big", the following lists factor into supplying the list of m words:

  • List of words following "my big" (this is given the highest weight)
  • List of words following "big" (next highest weight)
  • List of words occurring two after "my" (lower weight)
  • List of words occurring most frequently overall in the source (this list never changes and is a fallback when, as often happens with shorter sources, the other three lists are bare)

A similar pattern holds for higher values of n, with larger n-grams emphasized ver smaller n-grams, and closer n-grams emphasized over more distant ones.