Discriminative training using boosted lasso
First Claim
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1. A method comprising:
- setting a limit for the amount by which feature weights can be changed during a single iteration of training of feature weights in a language model;
selecting a feature weight from the set of feature weights;
computing a best value for the selected feature weight, wherein the best value comprises a value that results in the greatest change in a function, and wherein the best value differs from a previous value for the selected feature weight by a change amount;
determining if the absolute value of the change amount is less than the limit;
selecting the best value for the selected feature weight instead of a step-change value for the selected feature weight as a new value for the selected feature weight if the absolute value of the change amount is less than the limit, wherein the step-change value is formed by increasing the absolute value of the previous value of the feature weight by the limit; and
storing the new value for the feature weight as part of a current set of feature weights for the language model.
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Abstract
Word sequences that contain a selected feature are identified using an index that comprises a separate entry for each of a collection of features in the language model, each entry identifying word sequences that contain the feature. The identified word sequences are used to compute a best value for a feature weight of the selected feature. A selection is made between the best value and a step-change value for the feature weight to produce a new value for the feature weight. The new value for the feature weight is then stored in a current set of feature weights for the language model.
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Citations
20 Claims
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1. A method comprising:
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setting a limit for the amount by which feature weights can be changed during a single iteration of training of feature weights in a language model; selecting a feature weight from the set of feature weights; computing a best value for the selected feature weight, wherein the best value comprises a value that results in the greatest change in a function, and wherein the best value differs from a previous value for the selected feature weight by a change amount; determining if the absolute value of the change amount is less than the limit; selecting the best value for the selected feature weight instead of a step-change value for the selected feature weight as a new value for the selected feature weight if the absolute value of the change amount is less than the limit, wherein the step-change value is formed by increasing the absolute value of the previous value of the feature weight by the limit; and storing the new value for the feature weight as part of a current set of feature weights for the language model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 12)
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9. A computer-readable medium having computer-executable instructions for performing steps comprising:
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selecting a feature of a language model; identifying word sequences that contain the feature using an index that comprises a separate entry for each of a collection of features in the language model, each entry identifying word sequences that contain the feature; using the identified word sequences to compute a best value for a feature weight of the selected feature; selecting one of the best value and a step-change value for the feature weight as a new value for the feature weight; and storing the new value for the feature weight in a current set of feature weights for the language model. - View Dependent Claims (10, 11, 13, 14, 15)
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16. A method comprising:
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applying a feature for a language model to an index comprising a separate entry for each feature of the language model to identify a plurality of word sequences that contain the feature; using features contained in at least one of the identified word sequences to compute a word sequence exponential loss function; using the word sequence exponential loss function to determine a value for a feature weight for the feature; and storing the value for the feature weight as part of a language model. - View Dependent Claims (17, 18, 19, 20)
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Specification