Unified treatment of data-sparseness and data-overfitting in maximum entropy modeling
First Claim
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1. A computer-implemented method for modeling spoken language for a conversational dialog system, comprising:
- modeling, by a computer processor, dependency relations of the spoken language via a probabilistic dependency model;
incorporating, by the computer processor, Gaussian priors during feature selection and during parameter optimization;
parsing, by the computer processor, a sequence of words, the parsing including systematically searching through pairs of head words bottom-up using a chart parsing technique; and
at each step in the search, computing, by the computer processor, the probabilistic scores for each pair based on the probabilistic dependency model and keeping n best candidate pairs for each region;
wherein the dependency model is decomposed into a model for a first sub-region, a second sub-region, and a component which includes a last dependency relation that connects the first and second sub-regions, with an adjustment of mutual information between the last dependency relation and the first and second sub-regions.
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Abstract
A method of statistical modeling is provided which includes constructing a statistical model and incorporating Gaussian priors during feature selection and during parameter optimization for the construction of the statistical model.
26 Citations
18 Claims
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1. A computer-implemented method for modeling spoken language for a conversational dialog system, comprising:
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modeling, by a computer processor, dependency relations of the spoken language via a probabilistic dependency model; incorporating, by the computer processor, Gaussian priors during feature selection and during parameter optimization; parsing, by the computer processor, a sequence of words, the parsing including systematically searching through pairs of head words bottom-up using a chart parsing technique; and at each step in the search, computing, by the computer processor, the probabilistic scores for each pair based on the probabilistic dependency model and keeping n best candidate pairs for each region; wherein the dependency model is decomposed into a model for a first sub-region, a second sub-region, and a component which includes a last dependency relation that connects the first and second sub-regions, with an adjustment of mutual information between the last dependency relation and the first and second sub-regions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A dialog system comprising:
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a computer processor programmed to execute a spoken language understanding software module arrangement, the software module arrangement comprising; a speech recognizer module, which, when executed by the processor, causes the processor to recognize speech as a sequence of words; a part-of-speech tagger module, which, when executed by the processor, causes the processor to tag the sequence; a statistical dependency parser, which the processor is configured to use to form dependency structures for the sequence; a semantic mapper module, which, when executed by the processor, causes the processor to map grammatical features to the sequence; a topic classifier module, which, when executed by the processor, causes the processor to classify the sequence into at least one semantic category; and a dialog manager module, which, when executed by the processor, causes the processor to interpret meaning of the sequence in a context of a conversation; wherein the statistical dependency parser is based on dependency relations of the spoken language via a probabilistic dependency model, and wherein the dependency model is decomposed into a model for a first sub-region, a second sub-region, and a component which includes a last dependency relation that connects the first and second sub-regions, with an adjustment of mutual information between the last dependency relation and the first and second sub-regions. - View Dependent Claims (13, 14, 15, 16, 17)
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18. A computer-implemented method for modeling spoken language for a conversational dialog system, comprising:
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modeling, by a computer processor, dependency relations of the spoken language via a probabilistic dependency model; incorporating, by the computer processor, Gaussian priors during feature selection and during parameter optimization; parsing, by the computer processor, a sequence of words, the parsing including systematically searching through pairs of head words bottom-up using a chart parsing technique; and at each step in the search, computing, by the computer processor, the probabilistic scores for each pair based on the probabilistic dependency model and keeping n best candidate pairs for each region; wherein the dependency model is decomposed into a model for a left branch of a parse tree, a right branch of the parse tree, a conditional probability of a top level that connects the left and right branches, and an adjustment of mutual information between the top level and the left and right branches.
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Specification