PARSIMONIOUS HANDLING OF WORD INFLECTION VIA CATEGORICAL STEM + SUFFIX N-GRAM LANGUAGE MODELS
First Claim
1. A method for predicting words, the method comprising:
- at an electronic device;
receiving input from a user;
determining, using an n-gram language model, a probability of a predicted word based on a previously-input word in the received input, wherein the predicted word comprises a stem and a suffix;
determining, a probability of the suffix being grammatically valid for the stem;
determining an integrated probability of the predicted word based on the probability of the predicted word and the probability of the suffix being grammatically valid for the stem; and
providing output of the predicted word, based on the integrated probability.
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Abstract
Systems and processes are disclosed for predicting words using a categorical stem and suffix word n-gram language model. A word prediction includes determining a stem probability using a stem language model. The word prediction also includes determining a suffix probability using suffix language model decoupled from the stem model, in view of one or more stem categories. The word prediction also includes determine a probability of the stem belonging to the stem category. A joint probability is determined based on the foregoing, and one or more word predictions having sufficient likelihood. In this way, the categorical stem and suffix language model constraints predicted suffixes to those that would be grammatically valid with predicted stems, thereby producing word predictions with grammatically valid stem and suffix combinations.
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Citations
25 Claims
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1. A method for predicting words, the method comprising:
at an electronic device; receiving input from a user; determining, using an n-gram language model, a probability of a predicted word based on a previously-input word in the received input, wherein the predicted word comprises a stem and a suffix; determining, a probability of the suffix being grammatically valid for the stem; determining an integrated probability of the predicted word based on the probability of the predicted word and the probability of the suffix being grammatically valid for the stem; and providing output of the predicted word, based on the integrated probability. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer-readable storage medium comprising computer-readable instructions, which when executed by one or more processors, cause the one or more processors to:
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receive input from a user; determine, using an n-gram language model, a probability of a predicted word based on a previously-input word in the received input, wherein the predicted word comprises a stem and a suffix; determine, a probability of the suffix being grammatically valid for the stem; determine an integrated probability of the predicted word based on the probability of the predicted word and the probability of the suffix being grammatically valid for the stem; and provide output of the predicted word, based on the integrated probability.
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18. A system comprising:
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one or more processors; memory storing one or more programs, wherein the one or more programs include instructions, which when executed by the one or more processors, cause the one or more processors to; receive input from a user; determine, using an n-gram language model, a probability of a predicted word based on a previously-input word in the received input, wherein the predicted word comprises a stem and a suffix; determine, a probability of the suffix being grammatically valid for the stem; determine an integrated probability of the predicted word based on the probability of the predicted word and the probability of the suffix being grammatically valid for the stem; and provide output of the predicted word, based on the integrated probability. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25)
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Specification