PCFGs: Inside-Outside Probabilities Explained | Lecture 25

Added:

PCFG Recap
CKY Extension
Probability Calculation
Inner Algorithm
Probabilities Defined
Worked Example
Base Case
Inductive Step
Table Filling
Outside Algorithm

PCFG Recap

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Playing Section
  • 1

    Review of PCFG definition and key problems.

  • 2

    Focus on finding parse trees and sentence probabilities.

  • 3

    Introduction to the learning of rule probabilities.

Understanding of Context-Free Grammars (CFGs), Chomsky Normal Form (CNF), and standard parsing algorithms like the CKY (Cocke-Younger-Kasami) algorithm.
Foundational knowledge of probability theory, including joint, conditional, and marginal probabilities, as well as the chain rule.
Familiarity with Hidden Markov Models (HMMs) and the Forward-Backward algorithm, which serves as the sequential analog to the tree-based Inside-Outside algorithm.
Basic concepts of supervised parameter estimation, such as Maximum Likelihood Estimation (MLE) using annotated treebanks.
Application of the Expectation-Maximization (EM) algorithm for unsupervised grammar induction using the Inside-Outside probabilities.
Exploration of Lexicalized PCFGs (e.g., Collins and Charniak parsers) to address the structural and lexical independence assumptions of standard PCFGs.
Transition to Dependency Parsing models as a computationally efficient alternative to constituency-based PCFG parsing.
Study of Modern Neural PCFGs and deep learning-based parsing techniques that integrate vector representations with probabilistic grammar formalisms.
14.2K views73likes34:12@naturallanguageprocessing6035Original Release: 2017-02-15

The inside-outside algorithm computes sentence probabilities under probabilistic context-free grammars (PCFGs) by calculating two dynamic programming tables: inside probabilities (βj(p,q)) represent the probability that non-terminal Nj derives the substring from word p to q, while outside probabilities (αj(p,q)) represent the probability that the entire sentence derives from the start symbol given that Nj derives the substring. These probabilities are computed in a bottom-up manner for inside probabilities and top-down for outside probabilities, enabling efficient computation of sentence probabilities without enumerating all possible parse trees.