Language input architecture for converting one text form to another text form with tolerance to spelling, typographical, and conversion errors
First Claim
1. A language input architecture comprising:
- a user interface to receive an input string, the input string containing a spelling error; and
a typing model to generate a list of probable candidate strings that may be substituted for the input string based on typing error probabilities of how likely each of the candidate strings was incorrectly entered as the input string, the typing model being trained on actual data collected from multiple users entering at least one training text;
wherein the typing model is trained using a first language and further comprising a second typing model to generate a list of probable candidate strings that may be substituted for the input string based on typing error probabilities of how likely each of the candidate strings was incorrectly entered as the input string, the second typing model being trained in a second language.
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Abstract
A language input architecture converts input strings of phonetic text to an output string of language text. The language input architecture has a search engine, one or more typing models, a language model, and one or more lexicons for different languages. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string. The language model provides probable conversion strings for each of the typing candidates based on probabilities of how likely a probable conversion output string represents the candidate string. The search engine combines the probabilities of the typing and language models to find the most probable conversion string that represents a converted form of the input string.
247 Citations
9 Claims
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1. A language input architecture comprising:
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a user interface to receive an input string, the input string containing a spelling error; and
a typing model to generate a list of probable candidate strings that may be substituted for the input string based on typing error probabilities of how likely each of the candidate strings was incorrectly entered as the input string, the typing model being trained on actual data collected from multiple users entering at least one training text;
wherein the typing model is trained using a first language and further comprising a second typing model to generate a list of probable candidate strings that may be substituted for the input string based on typing error probabilities of how likely each of the candidate strings was incorrectly entered as the input string, the second typing model being trained in a second language. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification