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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

1

overview of applications

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

exercise: review grammar, refresh Python

2

UNIX command line tools

some Python

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

3

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

4

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

5

markov models

6

finite state transducers

7

CRFs

discriminative language models

8

text classification and clustering

9

recurrent neural networks

10

statistical alignment and MT

11

word sense disambiguation

12

parts-of-speech tagging

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

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)

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

make appointments with secretary@iupr.com

office hours

course assistants, TBD

professor, make appointment with secretary@iupr.com

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