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What are books about how to build a natural language parsing program like this:
input: I got to TALL you output: I got to TELL you input: Big RAT box output: Big RED box in: hoo un thum zend three out: one thousand three
It must have the language model that allows to predict what words are misspelled !
What are the best books on how to build such a tool??
p.s. Are there free webservices to spell-check? From Google maybe?..
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Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction.
Introduction. A language model in NLP is a probabilistic statistical model that determines the probability of a given sequence of words occurring in a sentence based on the previous words. It helps to predict which word is more likely to appear next in the sentence.
In the backdrop of machine learning, autocorrect is purely based on Natural Language Processing (NLP). As the name suggests that it is programmed in order to correct spellings and errors while typing text.
The isolated-word methods that will be described here are the most studied spelling correction algorithms, they are: edit distance [2], similarity keys [3], rule-based techniques [4], n-gram-based techniques[5], probabilistic techniques[6], neural networks[7] and noisy channel model [8,9].
Peter Norvig has written a terrific spell checker. Maybe that can help you.
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