Difference between revisions of "Inside Outside algorithm"

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For a given sentence of length <math>n</math> and grammar <math>G</math>, the inside outside algorithm is <math>O(Gn^3)</math>
 
For a given sentence of length <math>n</math> and grammar <math>G</math>, the inside outside algorithm is <math>O(Gn^3)</math>
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== References ==
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[[http://asadl.org/jasa/resource/1/jasman/v65/iS1/pS132_s1 | p]] The original paper introducing the inside-outside algorithm
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[[http://www.cs.jhu.edu/~jason/465/iobasics.pdf | Notes on Inside-Outside algorithm by Jason Eisner]] provides a good walkthrough and explanation on using the inside-outside algorithm with a PCFG

Revision as of 12:14, 29 November 2011

This is a Method page for the Inside-outside algorithm.

Background

The inside-outside algorithm is a way of estimating probabilities in a PCFG. It is first introduced [| Baker, 1979]. The inside outside algorithm is in fact a generalization of the forward-backward algorithm (for hidden Markov models) to PCFGs.

Algorithm

The algorithm is a dynamic programming algorithm that is often used with chart parsers to estimate expected production counts. Here, we assume the grammar is of Chomsky Normal Form.

The algorithm works by computing 2 probabilities for each nonterminal and span .

Inside probabilities

The inside probability is defined as , which is the probability of a nonterminal generating the word sequence to .

The inside probability can be calculated recursively with the following recurrence relation:

Intuitively, this can be seen as computing the sum over all possible ways of building trees rooted by and generating the word span .

For the base case, it is simply .

Outside counts

The outside probability is defined as , which is the probability of generating a parse tree spanning the entire sentence that uses nonterminal to span .

The reccurrence relation is thus:

The first term is basically considering all ways of generating trees where is used as a right subtree, and vis a vis for the second term.

Dynamic programming: Putting them together

In a standard EM framework, we would want to compute for each production rule, the expected number of times it is used for a given sentence, which we can compute by summing over the counts of using the production for all possible spans (and separation points)

Complexity

For a given sentence of length and grammar , the inside outside algorithm is

References

[| p] The original paper introducing the inside-outside algorithm

[| Notes on Inside-Outside algorithm by Jason Eisner] provides a good walkthrough and explanation on using the inside-outside algorithm with a PCFG