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Understanding Natural Language Processing: A Comprehensive Midterm Review

Dive into key concepts such as statistical vs symbolic processing, morphology, syntax, language modeling, probabilistic parsing, machine learning, semantics, and more. This review covers essential topics to enhance your grasp of NLP principles and techniques.

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Understanding Natural Language Processing: A Comprehensive Midterm Review

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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 • Inflectional v. Derivational • Affixation, infixation, concatenation • Morphotactics

  3. Morphological parsing • Koskenniemi’s two-level morphology • Porter stemmer • Minimum Edit Distance (Levenshtein) • N-grams • Markov assumption • Chain Rule • Language Modeling • Simple, Adaptive, Class-based (syntax-based), bursty • Smoothing • Add-one, Witten-Bell, Good-Turing • Back-off • Perplexity, Entropy • Maximum Likelihood Estimation

  4. 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 • Types of Ambiguity • Context Free Grammars • Top-down v. Bottom-up Derivations • Left Corners • Grammar Equivalence • Normal Forms (CNF)

  5. Probabilistic Parsing • (p)CYK, Earley Parsing • Derivational Probability • Lexicalization • Classification • Supertagging • Machine Learning • Dependent v. Independent variables • Training v. Development Test v. Test sets • Feature Vectors • Metrics • Accuracy • Precision, Recall, F-Measure • Gold Standards

  6. Semantics • Meaning Representations • Semantic Roles, Subcategorization frames • FOPC • Pros • Cons • Temporal Representations • Richenbach • Aspect • Beliefs, Desires, Intention Representation • Syntax-driven semantics

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