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Exploring Natural Language Processing Techniques and Models for Effective Language Understanding

This review delves into Regular Expressions, Finite State Automata, Finite State Transducers, Morphology, POS tagging, Language Modeling, Syntax, Semantics, and more. Learn about different approaches and models to enhance language processing.

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Exploring Natural Language Processing Techniques and Models for Effective Language Understanding

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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 POS • Inflectional v. Derivational • Affixation, infixation, concatenation • Morphotactics • Different languages, different morphologies • Evidence from human performance

  3. Noisy channel model • Bayesian inference • N-grams • Markov assumption • Chain Rule • Language Modeling • Simple, Adaptive, Class-based (syntax-based) • Smoothing • Add-one, Witten-Bell, Good-Turing • Back-off models • Creating and using ngram LMs • Corpora • Maximum Likelihood Estimation

  4. Testing and Training • How to choose a corpus • How to divide • Part-of-Speech Tagging • Hand Written Rules v. Statistical v. Hybrid • Brill Tagging • HMMs • Syntax • Parse Trees • Constituent Structure vs. Dependency Structure • What is a good parse tree? • Types of Ambiguity

  5. Context Free Grammars • Top-down v. Bottom-up Derivations • Early Algorithm • Normal Forms (CNF) • Modifying the grammar • Probabilistic Parsing • Derivational Probability • Computing probabilities for a rule • Choosing a rule probabilistically • Lexicalized head driven grammars

  6. Semantics • Where it fits • Thematic roles • First Order Predicate Calculus as a representation • Compositional Semantics • Word relations • Word sense disambiguation • Naïve Bayes • Decision rules • Lesk simplified and corpus • Selectional restrictions

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