Difference between revisions of "Inside Outside algorithm"
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The inside-outside algorithm is a way of estimating probabilities in a PCFG. It is first introduced [[http://asadl.org/jasa/resource/1/jasman/v65/iS1/pS132_s1 | Baker, 1979]]. The inside outside algorithm is in fact a generalization of the forward-backward algorithm (for hidden Markov models) to PCFGs. | The inside-outside algorithm is a way of estimating probabilities in a PCFG. It is first introduced [[http://asadl.org/jasa/resource/1/jasman/v65/iS1/pS132_s1 | Baker, 1979]]. The inside outside algorithm is in fact a generalization of the forward-backward algorithm (for hidden Markov models) to PCFGs. | ||
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== Algorithm == | == Algorithm == | ||
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<math>count(A\rightarrow BC)=\dfrac{p(A\rightarrow BC)}{\alpha(S,1,n)}\sum_{1\leq i \leq j \leq k \leq n} \beta(A,i,k)\alpha(B,i,j)\alpha(C,j+1,k)</math> | <math>count(A\rightarrow BC)=\dfrac{p(A\rightarrow BC)}{\alpha(S,1,n)}\sum_{1\leq i \leq j \leq k \leq n} \beta(A,i,k)\alpha(B,i,j)\alpha(C,j+1,k)</math> | ||
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+ | == Time complexity == | ||
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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> |
Revision as of 12:12, 29 November 2011
This is a Method page for the Inside-outside algorithm.
Contents
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)
Time complexity
For a given sentence of length and grammar , the inside outside algorithm is