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Natural Language Processing and Applications by Mind Map: Natural Language Processing and Applications
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Natural Language Processing and Applications

outline

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not covered

lexical acquisition

probabilistic context free grammars

probabilistic parsing

clustering

exam preparation

after each lecture...

identify definitions and concepts

identify mathematical proofs and derivations that were covered

identify algorithms that were covered

identify major concepts/tools that were covered

make a written list of definitions, concepts, proofs, derivations, and algorithms that you studied and bring them to the oral exam

during the exam

reading

Manning and Schütze: Foundations of Statistical NLP

Bird and Klein: Natural Language Processing in Python

Perkins: Python Text Processing with NLTK 2.0 Cookbook

Russell: Mining the Social Web (additional reading)

course mechanics

lectures: Room 48-462 Time Wednesdays, 1:45 - 3:15 pm

exercises: TBD

exam: oral

office hours

http://nlpa.iupr.com/

exercises

required for taking the oral exam

bring the completed exercises with you to the oral exam (otherwise you can't take the exam)

Exercises are mostly in Python, with some theory.

Do your exercises in iPython notebooks and hand them in.

Many exercises will give you a Python notebook as a starting point.

presentation on application examples