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



overview of applications

linguistic essentials, history of language, language variation, basic grammatical concepts

exercise: review grammar, refresh Python


UNIX command line tools

some Python

mathematical foundations, probability theory, common distributions, Bayesian methods, information theory


corpora, what are corpora and how are they used?, what corpora are available?, tokenization, lemmatization, stemming, markup, tagging


n-gram models, collocations, statistical models, sparsity, smoothing, back-off, cross-validation and testing


markov models


finite state transducers



discriminative language models


text classification and clustering


recurrent neural networks


statistical alignment and MT


word sense disambiguation


parts-of-speech tagging

not covered

lexical acquisition

probabilistic context free grammars

probabilistic parsing


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

For any definition/concept, you should be able to give a concise and clear definition in the exam

For any derivation that we covered in class or the exercises, you should be able to repeat it in the exam and also solve similar problems.

For any covered algorithm, you should be able to state the inputs/outputs, and the sequence of steps.

You also need to be able to demonstrate that you know the tools that have been used in the exercises (UNIX/POSIX command line tools, Python)


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

make appointments with

office hours

course assistants, TBD

professor, make appointment with


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