Method for dynamic context scope selection in hybrid n-gram+LSA language modeling
First Claim
Patent Images
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, wherein the vector representation of the current document in an LSA space is based upon a plurality of temporally ordered words and is generated from at least one decomposition matrix of a singular value decomposition of a co-occurrence matrix, W, between M words in a vocabulary V and N documents in a text corpus T;
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.
251 Citations
44 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, wherein the vector representation of the current document in an LSA space is based upon a plurality of temporally ordered words and is generated from at least one decomposition matrix of a singular value decomposition of a co-occurrence matrix, W, between M words in a vocabulary V and N documents in a text corpus T;
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, 21, 22)
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4. The method of claim 1 wherein the vector representation of the current document in an LSA space is based upon all words from a beginning of a session.
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5. The method of claim 4 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
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p is the normalized entropy of the word observed at time p within a text T, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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6. The method of claim 1 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
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i
p is the normalized entropy of the word observed at time p within a text T, P is the number of temporally adjacent words up to the current word, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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7. The method of claim 1 wherein the vector representation of the current document in an LSA space is based upon a plurality of exponentially weighted temporally ordered words.
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8. The method of claim 7 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
-
i
p is the normalized entropy of the word observed at time p within a text T, 0<
λ
≦
1, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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9. The method of claim 1 wherein the plurality of global probabilities is based upon a latent semantic paradigm.
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10. The method of claim 1 wherein the plurality of global probabilities Pr(wq|Hq−
- 1) for a particular word wq, for an associated history of the word, Hq−
1, for the current document {tilde over (d)}q−
i, as;
- 1) for a particular word wq, for an associated history of the word, Hq−
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11. The method of claim 10 wherein combining the local probabilities and the global probabilities is computed as follows:
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21. The method of claim 1 wherein the plurality of global probabilities Pr(wq|Hq−
- 1) for a particular word wq, for an associated history of the word, Hq−
1, for the current document {tilde over (d)}q−
i, as;
Pr(wq|Hq−
1)=Pr(wq|{tilde over (d)}q−
i),based upon the vector representation of the current document in an LSA space.
- 1) for a particular word wq, for an associated history of the word, Hq−
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22. The method of claim 21 wherein combining the local probabilities and the global probabilities is computed as follows:
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12. 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 based upon a plurality of temporally ordered words and is generated from at least one decomposition matrix of a singular value decomposition of a co-occurrence matrix, W, between M words in a vocabulary V and N documents in a text corpus T;
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. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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15. The method of claim 12 wherein the vector representation of the current document in an LSA space is based upon all words from a beginning of a session.
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16. The method of claim 15 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
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i
p is the normalized entropy of the word observed at time p within a text T, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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17. The method of claim 12 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
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i
p is the normalized entropy of the word observed at time p within a text T, P is the number of temporally adjacent words up to the current word, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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18. The system of claim 12 wherein the vector representation of the current document in an LSA space is based upon a plurality of exponentially weighted temporally ordered words.
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19. The method of claim 18 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
-
i
p is the normalized entropy of the word observed at time p within a text T, 0<
λ
≦
1, μ
ip is the left singular vector at time p of the singular value decomposition of W, as;
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20. The method of claim 12 wherein the plurality of global probabilities is based upon a latent semantic paradigm.
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23. 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 based upon a plurality of temporally ordered words and is generated from at least one decomposition matrix of a singular value decomposition of a co-occurrence matrix, W, between M words in a vocabulary V and N documents in a text corpus T;
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 (24, 25, 26, 27, 28, 29, 30, 31, 32, 33)
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26. The computer-readable medium of claim 23 wherein the vector representation of the current document in an LSA space is based upon all words from a beginning of a session.
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27. The computer-readable medium of claim 26 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
-
i
p is the normalized entropy of the word observed at time p within a text T, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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28. The computer-readable medium of claim 23 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
-
i
p is the normalized entropy of the word observed at time p within a text T, P is the number of temporally adjacent words up to the current word, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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29. The computer-readable medium of claim 23 wherein the vector representation of the current document in an LSA space is based upon a plurality of exponentially weighted temporally ordered words.
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30. The computer-readable medium of claim 29 wherein the vector representation of the current document in an LSA space, vq, at time q, wherein nq is the total number of words in the current document, ip is the index of the word observed at time p, ε
-
i
p is the normalized entropy of the word observed at time p within a text T, 0<
λ
≦
1, μ
ip is the left singular vector at time p of the singular value decomposition of W, and S is the diagonal matrix of singular values of the singular value decomposition of W, as;
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31. The computer-readable medium of claim 23 wherein the plurality of global probabilities is based upon a latent semantic paradigm.
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32. The computer-readable medium of claim 23 wherein the plurality of global probabilities Pr(wq|Hq−
- 1) for a particular word wq, for an associated history of the word, Hq−
1, for the current document {tilde over (d)}q−
i, as;
- 1) for a particular word wq, for an associated history of the word, Hq−
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33. The computer-readable medium of claim 32 wherein combining the local probabilities and the global probabilities is computed as follows:
- 34. 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 local 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, wherein the processor is further configured to generate the vector representation of the current document in an LSA space based upon a plurality of temporally ordered words from at least one decomposition matrix of a singular value decomposition of a co-occurrence matrix, W, between M words in a vocabulary V and N documents in a text corpus T.
Specification