Method for dynamic context scope selection in hybrid N-GRAMlanguage modeling

• US 20070162276A1
• Filed: 02/23/2007
• Published: 07/12/2007
• Est. Priority Date: 03/10/2000
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Bellegarda, Jerome
• Granted Patent: US 7,720,673 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams

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.

Claims

1. A method of 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 scope of the current document is adjustable;

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 a language modeling.

2. The method of claim 1 wherein the plurality of local probabilities is based upon an n-gram paradigm.

3. The method of claim 1 wherein the plurality of local probabilities Pr(w_q|H_q^(t)) for a particular word w_q, drawn from a vocabulary V comprising a plurality of words w_i, given a hybrid contextual history H_q^(T) of n−

1 words w<sub>q−

1</sub>, w<sub>q−

2</sub>, . . . w<sub>q−

n+1</sub>, as;

Pr(w_q|H_q^(t))=Pr(w_q|w<sub>q−

1</sub>W<sub>q−

2</sub> . . . w<sub>q−

n+1</sub>).

4. The method of claim 1 wherein the vector representation of the current document in an LSA space 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.

5. The method of claim 4 wherein the vector representation of the current document in an LSA space is based upon all words from a beginning of a session.

6. The method of claim 5 wherein the vector representation of the current document in an LSA space, v_q, at time q, wherein n_q is the total number of words in the current document, i_p is the index of the word observed at time p, ε

_ip 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;

7. The method of claim 4 wherein the vector representation of the current document in an LSA space is based upon a plurality of temporally adjacent words.

8. The method of claim 7 wherein the vector representation of the current document in an LSA space, v_q, at time q, wherein n_q is the total number of words in the current document, i_p is the index of the word observed at time p, ε

_ip 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;

9. The method of claim 4 wherein the vector representation of the current document in an LSA space is based upon a plurality of exponentially weighted temporally adjacent words.

10. The method of claim 9 wherein the vector representation of the current document in an LSA space, v_q, at time q, wherein n_q is the total number of words in the current document, i_p is the index of the word observed at time p, ε

_ip 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;

11. The method of claim 1 wherein the plurality of global probabilities is based upon a latent semantic paradigm.

12. The method of claim 1 wherein the plurality of global probabilities Pr(w_q|H_q−

1) for a particular word w_q, for an associated history of the word, H<sub>q−

1</sub>, for the current document {tilde over (d)}<sub>q−

1</sub>, as;

Pr(w_q|H<sub>q−

1</sub>)=Pr(w_q|{tilde over (d)}<sub>q−

1</sub>),

based upon the vector representation of the current document in an LSA space.

13. The method of claim 12 wherein combining the local probabilities and the global probabilities is computed as follows:

14. A system for language modeling of a document comprising:

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 wherein the scope of the current document is adjustable;

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 a language modeling.

15. A computer readable medium comprising instructions, which when executed on a processor, perform a method for 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 scope of the current document is adjustable;

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 a language modeling.

16. A system for 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 wherein the scope of the current document is adjustable, 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 a language modeling.

17. The system of claim 16 wherein the processor is further configured to generate the plurality of local probabilities based upon an n-gram paradigm.

18. The system of claim 16 wherein the processor is further configured to generate the plurality of local probabilities Pr(w_q|H_q^(t)) for a particular word w_q, drawn from a vocabulary V comprising a plurality of words w_i, given a hybrid contextual history H_q^(T) of n−

1 words w<sub>q−

1</sub>, w<sub>q−

2</sub>, . . . w<sub>q−

n+1</sub>, as;

Pr(w_q|H_q^(t))=Pr(w_q|w<sub>q−

1</sub>W<sub>q−

2</sub> . . . w<sub>q−

n+1</sub>).

19. The system of claim 16 wherein the processor is further configured to generate the vector representation of the current document in an LSA space 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.

20. The system of claim 19 wherein the processor is further configured to generate the vector representation of the current document in an LSA space based upon all words from a beginning of a session.

21. The system of claim 20 wherein the processor is further configured to generate the vector representation of the current document in an LSA space, v_q, at time q, wherein n_q is the total number of words in the current document, i_p is the index of the word observed at time p, ε

_ip 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;

22. The system of claim 19 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 adjacent words.

23. The system of claim 22 wherein the processor is further configured to generate the vector representation of the current document in an LSA space, v_q, at time q, wherein n_q is the total number of words in the current document, i_p is the index of the word observed at time p, ε

_ip 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;

24. The system of claim 16 wherein the processor is further configured to generate the vector representation of the current document in an LSA space based upon a plurality of exponentially weighted temporally adjacent words.

25. The system of claim 16 wherein the processor is further configured to generate the vector representation of the current document in an LSA space, v_q, at time q, wherein n_q is the total number of words in the current document, i_p is the index of the word observed at time p, ε

_ip 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;

26. The system of claim 16 wherein the processor is further configured to generate the plurality of global probabilities based upon a latent semantic paradigm.

27. The system of claim 16 wherein the processor is further configured to generate the plurality of global probabilities Pr(w_q|H_q−

1) for a particular word w_q, for an associated history of the word, H<sub>q−

1</sub>, for the current document {tilde over (d)}<sub>q−

1</sub>, as;

Pr(w_q|H<sub>q−

1</sub>)=Pr(w_q|{tilde over (d)}<sub>q−

1</sub>),

based upon the vector representation of the current document in an LSA space.

28. The system of claim 27 wherein the processor is further configured to combine the local probabilities and the global probabilities as follows: