1 / 7

Midterm Review

Prepare for the CS4705 Natural Language Processing midterm with this comprehensive review covering essential topics such as Regular Expressions, Finite State Automata, and determinism. Explore finite state transducers, morphological concerns like inflection and derivation, and POS tagging methods including Brill tagging and HMMs. Understand language modeling techniques, smoothing methods, and parse tree structures. Gain insights on grammatical frameworks and probabilistic parsing. Essential for mastering N-grams, training sets, and evaluating performance metrics.

akiko
Télécharger la présentation

Midterm Review

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related