1. Midterm Review CS4705 Natural Language Processing

2. Statistical v. Symbolic Processing –80/20 Rule Regular Expressions Finite State Automata –Determinism v. non-determinism –(Weighted) Finite State Transducers Morphology –Word Classes and p.o.s. –Inflectional v. Derivational –Affixation, infixation, concatenation –Morphotactics Midterm Review

3. –Different languages, different morphologies –Evidence from human performance Morphological parsing –Koskenniemi’s two-level morphology –FSAs vs. FSTs –Porter stemmer Noise channel model –Bayesian inference Spelling correction –Bayesian approach

4. –Minimum Edit Distance (Levenshtein distance) Dynamic Programming N-grams –Markov assumption –Chain Rule –Language Modeling Simple, Adaptive, Class-based (syntax-based) Smoothing –Add-one, Witten-Bell, Good-Turing Back-off models

5. Creating and using ngram LMs –Corpora –Maximum Likelihood Estimation Syntax –Chomsky’s view: Syntax is cognitive reality –Parse Trees Dependency Structure –Part-of-Speech Tagging Hand Written Rules v. Statistical v. Hybrid Brill Tagging

6. –Types of Ambiguity Context Free Grammars –Top-down v. Bottom-up Derivations Left Corners –Grammar Equivalence –Normal Forms (CNF) Probabilistic Parsing –CYK parser –Derivational Probability –Lexicalization

7. Machine Learning –Dependent v. Independent variables –Training v. Development Test v. Test sets –Feature Vectors –Metrics Accuracy Precision, Recall, F-Measure –Gold Standards