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The updated slides for my introductory course on text mining. The slides take you from basic Bayesian statistics over Markov chains and language models (incl. neural embeddings) to text similarity measures (Locality Sensitive Hashing, Latent Semantic Analysis, and Latent Dirichlet Allocation) and classification techniques (Naïve Bayes, MaxEnt [logistic regression]). Finally, they introduce you to shallow parsing with dynamic graphical models (HMMs, MEMMs, and CRFs) and provide you with a glimpse at dependency parsing (shift-reduce, arc-standard).
The updated slides for my introductory course on text mining. The slides take you from basic Bayesian statistics over Markov chains and language models (incl. neural embeddings) to text similarity measures (Locality Sensitive Hashing, Latent Semantic Analysis, and Latent Dirichlet Allocation) and classification techniques (Naïve Bayes, MaxEnt [logistic regression]). Finally, they introduce you to shallow parsing with dynamic graphical models (HMMs, MEMMs, and CRFs) and provide you with a glimpse at dependency parsing (shift-reduce, arc-standard).
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