Method and system for providing alternatives for text derived from stochastic input sources
First Claim
1. A computer-implemented method for correcting text, comprising the steps of:
- receiving a text selection comprising a plurality of text components derived from different input sources, wherein at least one of the text components comprises a stochastic text component derived from a stochastic input source;
receiving a command to display alternatives for the text selection;
parsing the text selection into the text components;
retrieving the stochastic model for the stochastic text component from the at least one stochastic input source;
combining the stochastic model with other text components to produce a list of alternatives for the text selection, wherein the other text components include non-stochastic text components received from a non-stochastic input source; and
displaying the list of alternatives for the text selection on a display device, wherein the text selection comprises a plurality of stochastic text components and one of the stochastic models comprises an “
n-best”
candidate list and another stochastic model comprises a lattice, and wherein the step of combining the stochastic models to produce a list of alternatives for the text selection further comprises the steps of;
creating an “
n-best”
candidate list corresponding to the lattice; and
producing the list of alternatives for the text selection by combining the “
n-best”
candidate lists for the text components.
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Accused Products
Abstract
A computer-implemented method for providing a candidate list of alternatives for a text selection containing text from multiple input sources, each of which can be stochastic (such as a speech recognition unit, handwriting recognition unit, or input method editor) or non-stochastic (such as a keyboard and mouse). A text component of the text selection may be the result of data processed through a series of stochastic input sources, such as speech input that is converted to text by a speech recognition unit before being used as input into an input method editor. To determine alternatives for the text selection, a stochastic input combiner parses the text selection into text components from different input sources. For each stochastic text component, the combiner retrieves a stochastic model containing alternatives for the text component. If the stochastic text component is the result of a series of stochastic input sources, the combiner derives a stochastic model that accurately reflects the probabilities of the results of the entire series. The combiner creates a list of alternatives for the text selection by combining the stochastic models retrieved. The combiner may revise the list of alternatives by applying natural language principles to the text selection as a whole. The list of alternatives for the text selection is then presented to the user. If the user chooses one of the alternatives, then the word processor replaces the text selection with the chosen candidate.
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Citations
21 Claims
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1. A computer-implemented method for correcting text, comprising the steps of:
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receiving a text selection comprising a plurality of text components derived from different input sources, wherein at least one of the text components comprises a stochastic text component derived from a stochastic input source; receiving a command to display alternatives for the text selection; parsing the text selection into the text components; retrieving the stochastic model for the stochastic text component from the at least one stochastic input source; combining the stochastic model with other text components to produce a list of alternatives for the text selection, wherein the other text components include non-stochastic text components received from a non-stochastic input source; and displaying the list of alternatives for the text selection on a display device, wherein the text selection comprises a plurality of stochastic text components and one of the stochastic models comprises an “
n-best”
candidate list and another stochastic model comprises a lattice, and wherein the step of combining the stochastic models to produce a list of alternatives for the text selection further comprises the steps of;creating an “
n-best”
candidate list corresponding to the lattice; andproducing the list of alternatives for the text selection by combining the “
n-best”
candidate lists for the text components. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A computer-implemented method for correcting text, comprising the steps of:
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receiving a text selection from a user; receiving a command to display alternatives for the text selection; submitting the text selection to a correction scope model to determine if a scope of correction should be adjusted; if the correction scope model determines the scope of correction should be adjusted, then receiving from the correction scope model a text unit that includes the text selection and at least one adjacent word; producing a list of alternatives for the text unit wherein the step of producing a list of alternatives for the text unit further comprises the step of; parsing the text unit into text components derived from different input sources; determining whether at least one of the text components comprises a stochastic text component; retrieving a stochastic model for the stochastic text component; combining the stochastic model with other text components, wherein the other text components include non-stochastic text components received from a non-stochastic input source; and displaying the list of alternatives for the text unit on a display device, wherein the text selection comprises a plurality of stochastic text components and one of the stochastic models comprises an “
n-best”
candidate list and another stochastic model comprises a lattice, and wherein the step of combining the stochastic models to produce a list of alternatives for the text selection further comprises the steps of;creating an “
n-best”
candidate list corresponding to the laffice; andproducing the list of alternatives for the text selection by combining the “
n-best”
candidate lists for the text components. - View Dependent Claims (19, 20, 21)
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Specification