Course Syllabus
Introduction and Mathematical foundations: Elementary probability theory – Essential information theory. Linguistic essentials: Part of speech and morphology – Phrase structure. Corpus based work: Looking up text – Marked-up data. Statistical inference: Bins: Forming equivalence classes – Statistical Estimators – Combining Estimators. Word Sense Disambiguation: Supervised and Dictionary based Disambiguation. Markov Models: Hidden Markov Models – Implementation – Properties and Variants. Part of Speech Tagging: Hidden Markov Model Taggers – Transformation based Learning of Tags – Tagging accuracy and use of Taggers. Probabilistic Context free grammars and Probabilistic parsing. Statistical alignment and Machine translation: Text alignment – Word alignment – Statistical Machine Translation.