Download presentation

Presentation is loading. Please wait.

1
More on Text Management

2
Context Free Grammars Context Free Grammars are a more natural model for Natural Language Syntax rules are very easy to formulate using CFGs Provably more expressive than Finite State Machines – E.g. Can check for balanced parentheses

3
Context Free Grammars Non-terminals Terminals Production rules – V → w where V is a non-terminal and w is a sequence of terminals and non-terminals

4
Context Free Grammars Can be used as acceptors Can be used as a generative model Similarly to the case of Finite State Machines How long can a string generated by a CFG be?

5
Stochastic Context Free Grammar Non-terminals Terminals Production rules associated with probability – V → w where V is a non-terminal and w is a sequence of terminals and non-terminals

6
Chomsky Normal Form Every rule is of the form V → V1V2 where V,V1,V2 are non-terminals V → t where V is a non-terminal and t is a terminal Every (S)CFG can be written in this form Makes designing many algorithms easier

7
Questions What is the probability of a string? – Defined as the sum of probabilities of all possible derivations of the string Given a string, what is its most likely derivation? – Called also the Viterbi derivation or parse – Easy adaptation of the Viterbi Algorithm for HMMs Given a training corpus, and a CFG (no probabilities) learn the probabilities on derivation rule

8
Inside probability: probability of generating w p …w q from non-terminal N j. Outside probability: total prob of beginning with the start symbol N 1 and generating and everything outside w p …w q Inside-outside probabilities

9
CYK algorithm NjNj NrNr NsNs wpwp wdwd W d+1 wqwq

10
CYK algorithm NjNj NgNg wpwp wqwq W q+1 wewe NfNf N1N1 w1w1 wmwm

11
CYK algorithm NgNg NjNj wewe W p-1 WpWp wqwq NfNf N1N1 w1w1 wmwm

12
Outside probability

13
Probability of a sentence

14
The probability that a binary rule is used (1)

15
The probability that N j is used (2)

17
The probability that a unary rule is used (3)

18
Multiple training sentences (1) (2)

Similar presentations

© 2021 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google