Method for dynamic context scope selection in hybrid N-gramlanguage modeling
First Claim
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1. A method of dynamic language modeling of a document comprising:
- computing a plurality of local probabilities of a current document;
determining a vector representation of the current document in a latent semantic analysis (LSA) space;
computing a plurality of global probabilities based upon the vector representation of the current document in an LSA space; and
combining the local probabilities and the global probabilities to produce the language modeling.
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Abstract
A method and system for dynamic language modeling of a document are described. In one embodiment, a number of local probabilities of a current document are computed and a vector representation of the current document in a latent semantic analysis (LSA) space is determined. In addition, a number of global probabilities based upon the vector representation of the current document in an LSA space is computed. Further, the local probabilities and the global probabilities are combined to produce the language modeling.
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Citations
28 Claims
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1. A method of dynamic language modeling of a document comprising:
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computing a plurality of local probabilities of a current document;
determining a vector representation of the current document in a latent semantic analysis (LSA) space;
computing a plurality of global probabilities based upon the vector representation of the current document in an LSA space; and
combining the local probabilities and the global probabilities to produce the language modeling. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A system for dynamic language modeling of a document comprising:
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means for computing a plurality of local probabilities of a current document;
means for determining a vector representation of the current document in a latent semantic analysis (LSA) space;
means for computing a plurality of global probabilities based upon the vector representation of the current document in an LSA space; and
means for combining the local probabilities and the global probabilities to produce the language modeling.
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15. A computer readable medium comprising instructions, which when executed on a processor, perform a method for dynamic language modeling of a document, comprising:
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computing a plurality of local probabilities of a current document;
determining a vector representation of the current document in a latent semantic analysis (LSA) space;
computing a plurality of global probabilities based upon the vector representation of the current document in an LSA space; and
combining the local probabilities and the global probabilities to produce the language modeling.
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16. A system for dynamic language modeling of a document comprising:
a hybrid training/recognition processor configured to compute a plurality of local probabilities of a current document, determine a vector representation of the current document in a latent semantic analysis (LSA) space, compute a plurality of global probabilities based upon the vector representation of the current document in an LSA space, and combine the local probabilities and the global probabilities to produce the language modeling. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
Specification