Project Mashups (predictive text)

Assignment: Use predictive models to generate text: either a Markov chain or an RNN, or both. How does your choice of source text affect the output? Try combining predictive text with other methods we’ve used for analyzing and generating text: use RNN-generated text to fill Tracery templates, or train a Markov model on the output of parsing parts of speech from a text, or some other combination. What works and what doesn’t? How does RNN-generated text “feel” different from Markov-generated text? How does the length of the n-gram and the unit of the n-gram affect the quality of the output?

Original text reference:

Code: to be uploaded

'Intervention to its sequin surfactant towards people in these nuances one places once and 16 vibration be',
'Intervention (active installation to testing and music. Track the fundamentals of white light. The audio ',

 'Interactions and relation from Google Location of collaborative materials; clear. This project uses one p',

 'Interventions: Pet, energetically in reality by the the bigger picture of our minds try to engage with a ',

 'Interactive project between scents of light is asked together). The abstract morphing and plays with dist',

 'Intervention to them. By tuning to testing an associated using the installations creative installation vi',

 'Interactive means towards these are created using p5.js for determining to their motion, time and senses ',

 'Interacts with them. However, we never real time. The installation visuals Process and or uses taken a pr',

 'Interventions; how scents an arduino. Code: Senses agree, our minds ge',

 'Interactive community Day, New York 2019 ITP Wintervention to test human reaction patterns and sense of f']

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