Systems and methods for structured stem and suffix language models
First Claim
1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to:
- receive an input from a user;
determine, using a first n-gram language model, a first probability of a stem based at least on a first portion of a previously-input word in the received input;
determine, using a second n-gram language model, a second probability of a first suffix based at least on a second portion of the previously-input word in the received input;
determine, using a third n-gram language model, a third probability of a second suffix different from the first suffix based at least on a third portion of the previously-input word in the received input, wherein the third n-gram language model includes a tense suffix n-gram language model, and the determining of the third probability of the second suffix includes determining the third probability of a tense suffix based at least in part on a second tense suffix of the previously-input word;
determine a fourth probability of at least one predicted word based on the first probability, the second probability and the third probability; and
provide an output of the at least one predicted word to the user based on the fourth probability, wherein providing the output comprises at least one of displaying the predicted word or providing an audible playback of the predicted word.
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Abstract
Systems and methods are disclosed for predicting words using a structured stem and suffix n-gram language model. The systems and methods include determining, using a first n-gram word language model, a first probability of a stem based on a first portion of a previously-input word in the received input. Using a second n-gram language model, a second probability of a first suffix may be determined based at least on a second portion the previously-input word in the received input. Further, a third probability of a second suffix different from the first suffix may be determined using a third n-gram language model based at least on a third portion of the previously-input word in the received input. A fourth probability of a predicted word may be determined based on the first, second and third probabilities. One or more predicted words may be determined and provided as an output to the user.
3587 Citations
19 Claims
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1. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to:
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receive an input from a user; determine, using a first n-gram language model, a first probability of a stem based at least on a first portion of a previously-input word in the received input; determine, using a second n-gram language model, a second probability of a first suffix based at least on a second portion of the previously-input word in the received input; determine, using a third n-gram language model, a third probability of a second suffix different from the first suffix based at least on a third portion of the previously-input word in the received input, wherein the third n-gram language model includes a tense suffix n-gram language model, and the determining of the third probability of the second suffix includes determining the third probability of a tense suffix based at least in part on a second tense suffix of the previously-input word; determine a fourth probability of at least one predicted word based on the first probability, the second probability and the third probability; and provide an output of the at least one predicted word to the user based on the fourth probability, wherein providing the output comprises at least one of displaying the predicted word or providing an audible playback of the predicted word. - 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 method, comprising:
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at an electronic device; receiving an input from a user; determining, using a first n-gram language model, a first probability of a stem based at least on a first portion of a previously-input word in the received input; determining, using a second n-gram language model, a second probability of a first suffix based at least on a second portion of the previously-input word in the received input; determining, using a third n-gram language model, a third probability of a second suffix different from the first suffix based at least on a third portion of the previously-input word in the received input, wherein the third n-gram language model includes a tense suffix n-gram language model, and the determining of the third probability of the second suffix includes determining the third probability of a tense suffix based at least in part on a second tense suffix of the previously-input word; determining a fourth probability of at least one predicted word based on the first probability, the second probability and the third probability; and providing an output of the at least one predicted word to the user based on the fourth probability, wherein providing the output comprises at least one of displaying the predicted word or providing an audible playback of the predicted word.
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19. An electronic device comprising:
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one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for; receiving an input from a user; determining, using a first n-gram language model, a first probability of a stem based at least on a first portion of a previously-input word in the received input; determining, using a second n-gram language model, a second probability of a first suffix based at least on a second portion of the previously-input word in the received input; determining, using a third n-gram language model, a third probability of a second suffix different from the first suffix based at least on a third portion of the previously-input word in the received input, wherein the third n-gram language model includes a tense suffix n-gram language model, and the determining of the third probability of the second suffix includes determining the third probability of a tense suffix based at least in part on a second tense suffix of the previously-input word; determining a fourth probability of at least one predicted word based on the first probability, the second probability and the third probability; and providing an output of the at least one predicted word to the user based on the fourth probability, wherein providing the output comprises at least one of displaying the predicted word or providing an audible playback of the predicted word.
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Specification