Method for probabilistic error-tolerant natural language understanding
First Claim
1. A method of probabilistic error-tolerant natural language understanding, comprising:
- using a speech recognition to convert an utterance of a user into a possible word sequence set;
dividing a concept grammar into a static grammar and a dynamic grammar, wherein the static grammar is predetermined and is not variable according to the input word sequence set, while the dynamic grammar is formed by comparing the input word sequence set and the static grammar;
using the concept grammar to parse the word sequence set into a concept parse forest, wherein the concept parse forest further comprises at least one hypothetical concept sequence;
using at least one exemplary concept sequence to represent the concept sequence which is well-formed being recognized by the concept grammar; and
comparing the hypothetical concept sequences and the exemplary concept sequences to find the most possible concept sequence, and to convert the concept sequence into a semantic frame expressing intention of the user, wherein a probability based on characteristics of speech, grammar, example, and edit is used to find the most possible concept sequence.
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Abstract
A method of probabilistic error-tolerant natural language understanding. The process of language understanding is divided into a concept parse and a concept sequence comparison steps. The concept parse uses a parse driven by a concept grammar to construct a concept parse forest set by parsing results of speech recognition. The concept sequence comparison uses an error-tolerant interpreter to compare the hypothetical concept sequences included by the concept parse forest set and the exemplary concept sequences included in the database of the system. A most possible concept sequence is found and converted into a semantic framed that expresses the intention of the user. The whole process is led by a probability oriented scoring function. When error occurs in the speech recognition and a correct concept sequence cannot be formed, the position of the error is determined and the error is recovered according to the scoring function to reduce the negative effect.
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Citations
14 Claims
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1. A method of probabilistic error-tolerant natural language understanding, comprising:
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using a speech recognition to convert an utterance of a user into a possible word sequence set;
dividing a concept grammar into a static grammar and a dynamic grammar, wherein the static grammar is predetermined and is not variable according to the input word sequence set, while the dynamic grammar is formed by comparing the input word sequence set and the static grammar;
using the concept grammar to parse the word sequence set into a concept parse forest, wherein the concept parse forest further comprises at least one hypothetical concept sequence;
using at least one exemplary concept sequence to represent the concept sequence which is well-formed being recognized by the concept grammar; and
comparing the hypothetical concept sequences and the exemplary concept sequences to find the most possible concept sequence, and to convert the concept sequence into a semantic frame expressing intention of the user, wherein a probability based on characteristics of speech, grammar, example, and edit is used to find the most possible concept sequence. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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Specification