This thesis presents an empirical comparison between PPM★ (Prediction by Partial Matching), a state-of-the-art statistical language modeling algorithm, and IDyOT (Information Dynamics of Thinking), a cognitive architecture designed to model human-like hierarchical learning and prediction.
Implementation of classic N-Gram Language Models with Laplace smoothing, interpolation backoff, and perplexity evaluation — built from scratch in Python.
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