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Comprehensive Review of Natural Language Processing Concepts for CS4705 Midterm Exam

This review encapsulates essential topics for the CS4705 Natural Language Processing midterm. It covers both statistical and symbolic processing approaches, exploring fundamental concepts like regular expressions, finite state automata, and the nuances between determinism and non-determinism. Key topics such as morphology, word classes, parsing techniques, language modeling, and probabilistic parsing are discussed in detail. Additionally, it addresses Bayesian inference, noise channel models, n-grams, and evaluation metrics like accuracy and F-measure crucial for NLP. Prepare effectively with this structured overview.

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Comprehensive Review of Natural Language Processing Concepts for CS4705 Midterm Exam

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  1. Midterm Review CS4705 Natural Language Processing

  2. Midterm Review • 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

  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

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