January 2012 Spelling Models 1

Human Language Technology

Spelling Models

January 2012 Spelling Models 2

References

• Eric Mays, Fred J. Damerau, and Robert L. Mercer. 1991. Context based spelling correction. Inf. Process. Manage. 27, 5 (September 1991), 517-522.

• Church, K. and W. Gale (1991). Probability Scoring for Spelling Correction. Statistics and Computing 1: 93-103.

• Brill, E. and Moore, R., (2000), An improved error model for noisy channel spelling correction, Proceedings of ACL Conference, [pdf]

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Outline

• In this lecture we describe three different models of how spelling errors are produced.

• Single Character– Equal probabililty– Differentiated probability

• Multiple Character

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Confusion Set

The confusion set of a word w includes w along with all words in the dictionary D such that O can be derived from w by a single application of one of the four edit operations: – Add a single letter.– Delete a single letter.– Replace one letter with another.– Transpose two adjacent letters.

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Error Model 1Mayes, Damerau et al. 1991

• Let C be the number of words in the confusion set of w.

• The error model, for all s in the confusion set of d, is:P(O|w) = α if O=w,

(1- α)/(C-1) otherwise• α is the prior probability of a given typed word

being correct.• Key Idea: The remaining probability mass is

distributed evenly among all other words in the confusion set.

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Error Model 2: Church & Gale 1991

• Church & Gale (1991) propose a more sophisticated error model based on same confusion set (one edit operation away from w).

• Two improvements:1. Unequal weightings attached to different editing

operations.2. Insertion and deletion probabilities are conditioned

on context. The probability of inserting or deleting a character is conditioned on the letter appearing immediately to the left of that character.

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Obtaining Error Probabilities

• The error probabilities are derived by first assuming all edits are equiprobable.

• They use as a training corpus a set of space-delimited strings that were found in a large collection of text, and that (a) do not appear in their dictionary and (b) are no more than one edit away from a word that does appear in the dictionary.

• They iteratively run the spell checker over the training corpus to find corrections, then use these corrections to update the edit probabilities.

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Error Model 3Brill and Moore (2000)

• Let Σ be an alphabet• Model allows all operations of the form

α β, where α,β in Σ*. • P(α β) is the probability that when users

intends to type the string α they type β instead.

• N.B. model considers substitutions of arbitrary substrings not just single characters.

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Model 3Brill and Moore (2000)

• Model also tries to account for the fact that in general, positional information is a powerful conditioning feature, e.g. p(entler|antler) < p(reluctent|reluctant)

• i.e. Probability is partially conditioned by the position in the string in which the edit occurs.

• artifact/artefact; correspondance/correspondence

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Three Stage Model

• Person picks a word.physical

• Person picks a partition of characters within word.ph y s i c al

• Person types each partition, perhaps erroneously.

• f i s i k le• p(fisikle|physical) =

p(f|ph) * p(i|y) * p(s|s) * p(i|i) * p(k|c) * p(le|al)

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Formal Presentation

∑ ∏∑∈ =

=∈)(

||

1||||

)(

)|()|(wPartR

R

i

ii

RTsPartT

RTPwRP

• Let Part(w) be the set of all possible ways to partition string w into substrings.

• For particular R in Part(w) containing j continuous segments, let Ri be the ith segment. Then P(s|w) =

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Simplification

∏=

||

1

R

i

P(s | w) =max R

P(R|w) P(Ti|Ri)

• By considering only the best partitioning of s and w this simplifies to

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Training the Model

• To train model, need a series of (s,w) word pairs.

• begin by aligning the letters in (si,wi) based on MED.

• For instance, given the training pair (akgsual, actual), this could be aligned as:a c t u a l

a k g s u a l

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Training the Model

• This corresponds to the sequence of editing operations

• aa ck εg ts uu aa ll• To allow for richer contextual information, each

nonmatch substitution is expanded to incorporate up to N additional adjacent edits.

• For example, for the first nonmatch edit ck in the example above, with N=2, we would generate the following substitutions:

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Training the Model

a c t u a l

a k g s u a l

c kac akc kgac akgct kgs

• We would do similarly for the other nonmatch edits, and give each of these substitutions a fractional count.

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Training the Model

• We can then calculate the probability of each substitution α β ascount(α β)/count(α).

• count(α β) is simply the sum of the counts derived from our training data as explained above

• Estimating count(α) is harder, since we are not training from a text corpus, but from a a set of (s,w) tuples (without an associated corpus)

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Training the Model

• From a large collection of representative text, count the number of occurrences of α.

• Adjust the count based on an estimate of the rate with which people make typing errors.