Transcript Lecture 3 summary

Lecture 11 Summary
•
Detecting and correcting spelling errors is an important issue in spoken language processing systems.
•
It can be broken down into three problems:
•
Non-word error detection - detecting errors resulting in non-words.
•
Isolated-word error detection - detecting errors resulting in non-words in isolation.
•
Context dependent error detection - Using the neighboring context to detect and correct errors
even if they result in words from the lexicon.
•
Much of the research in correcting spelling errors (typographic and cognitive) is focused on singleerror misspellings.
•
Probabilistic models for detecting pronunciation and spelling variation comprise of using concepts
from Bayesian classification – attempting to find the correct word from the lexicon, given the ‘noisy’
word (with spelling/pronunciation variations).
Estimate of the correct word
wˆ  arg max P( w / O)
Observed word
Every word in the lexicon
wV
•
Using Bayes’ rule:
wˆ  arg max
wV
P(O / w) P( w)
 arg max P(O / w) P( w)
P(O)
wV
Likelihood
ECE 7000 Natural Language Processing
Prior
Page 1 of 1