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Midterm Review

Dive into the core concepts of natural language processing, including morphology, syntax, and machine learning techniques. From regular expressions to probabilistic parsing, gain a comprehensive understanding of NLP fundamentals.

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Midterm Review

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  1. Midterm Review CS4705 Natural Language Processing

  2. Midterm Review • 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

  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

  4. 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 • What is a good parse tree? • Part-of-Speech Tagging • Hand Written Rules v. Statistical v. Hybrid • Brill Tagging • HMMs

  6. Types of Ambiguity • Context Free Grammars • Top-down v. Bottom-up Derivations • Early Algorithm • Grammar Equivalence • Normal Forms (CNF) • Modifying the grammar • Probabilistic Parsing • Derivational Probability • Computing probabilities for a rule • Choosing a rule probabilistically • 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 • Semantics • Where it fits • Thematic roles • First Order Predicate Calculus as a representation • Semantic Analysis will not be covered on the midterm

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