Similarity metric for semantic profiling

US 20070073745A1
Filed: 05/31/2006
Published: 03/29/2007
Est. Priority Date: 09/23/2005
Status: Abandoned Application

First Claim

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1. A computer-implemented method of determining similarity between portions of text, the method comprising:

generating a semantic profile for at least two portions of text, each semantic profile comprising a vector of values; and

computing a similarity metric representing a similarity between the at least two portions of text using the at least two generated semantic profiles.

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Abstract

The method, system, and computer program provides the capability to compute measurements of the similarity between portions of text based on semantic profiles of the text portions. A computer-implemented method of determining similarity between portions of text comprises generating a semantic profile for at least two portions of text, each semantic profile comprising a vector of values and computing a similarity metric representing a similarity between the at least two portions of text using the at least two generated semantic profiles. The semantic profile comprises a vector of information values.

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36 Claims

1. A computer-implemented method of determining similarity between portions of text, the method comprising:
- generating a semantic profile for at least two portions of text, each semantic profile comprising a vector of values; and
  
  computing a similarity metric representing a similarity between the at least two portions of text using the at least two generated semantic profiles.
- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
- - 2. The method of claim 1, wherein the semantic profile comprising a vector of information values.
  - 3. The method of claim 2, wherein a portion of text comprises a sentence.
  - 4. The method of claim 2, wherein a portion of text comprises a paragraph.
  - 5. The method of claim 2, wherein the similarity metric is computed according to:
    - $extended cosine similarity metric = \frac{V_{1} \cdot V_{2}}{ V_{1}  \times  V_{2} }, where$ $ V  = \sqrt{\sum_{i = 1}^{n} x^{2}}, V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 6. The method of claim 2, wherein t the similarity metric is computed according to:
    - $extended dice similarity metric = \frac{V_{1} \cdot V_{2}}{(\sum_{i = 1}^{n} x_{i}^{2} + \sum_{i = 1}^{n} y_{i}^{2}) / 2}, and where$ $V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 7. The method of claim 2, wherein the similarity metric is computed according to:
    - $extended jaccard similarity metric = \frac{V_{1} \cdot V_{2}}{(\sum_{i = 1}^{n} x_{i}^{2} + \sum_{i = 1}^{n} y_{i}^{2}) - (V_{1} \cdot V_{2})}, where$ $V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 8. The method of claim 2, wherein the similarity metric is computed according to:
    - $z - method similarity metric = \sum_{i = 1}^{n} (a_{i} + b_{i}) \times c_{i}, where$ $a_{i} = \max (x_{i}, y_{i}), b_{i} = \min (x_{i}, y_{i}), c_{i} = \frac{b_{i}}{a_{i}},$ and x, y are information values.
  - 9. The method of claim 8, wherein the similarity metric is further computed according to:
    - if (a_i+b_i)×
      
      c_i≧
      
      a_i, then substitute b_ifor (a_i+b_i)×
      
      c_i.
  - 10. The method of claim 8, wherein the similarity metric is further computed according to:
    - if (a_i+b_i)×
      
      c_i≧
      
      a_i, then substitute $\frac{(a_{i} + b_{i})}{2}$ for (a_i+b_i)×
      
      c_i.
  - 11. The method of claim 2, wherein the similarity metric is computed according to:
    - $L_{1} norm similarity metric = \frac{1}{2} L_{1} (p, q), wherein$ $L_{1} = \sum_{i} \langle p_{i} - q_{i} \rangle,$ and wherein p, q are conditional probabilities of information values between two semantic profiles computed as an information value for each semcode in a profile divided by a sum of information values for that profile.
  - 12. The method of claim 2, wherein the similarity metric is computed according to:
    - similarity metric=iv_sc,d×
      
      idv_sc, wherein iv_sc,dis an information value of a semcode sc in a document d, and idv_scis a value that a semcode sc has across an entire collection.

13. A system for determining similarity between portions of text comprising:
- a processor operable to execute computer program instructions;
  
  a memory operable to store computer program instructions executable by the processor; and
  
  computer program instructions stored in the memory and executable to perform the steps of;
  
  generating a semantic profile for at least two portions of text, each semantic profile comprising a vector of values; and
  
  computing a similarity metric representing a similarity between the at least two portions of text using the at least two generated semantic profiles.
- View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
- - 14. The system of claim 13, wherein the semantic profile comprising a vector of information values.
  - 15. The system of claim 14, wherein a portion of text comprises a sentence.
  - 16. The system of claim 14, wherein a portion of text comprises a paragraph.
  - 17. The system of claim 14, wherein the similarity metric is computed according to:
    - $extended cosine similarity metric = \frac{V_{1} \cdot V_{2}}{ V_{1}  \times  V_{2} }, where$ $ V  = \sqrt{\sum_{i = 1}^{n} x^{2}}, V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 18. The system of claim 14, wherein t the similarity metric is computed according to:
    - $extended dice similarity metric = \frac{V_{1} \cdot V_{2}}{(\sum_{i = 1}^{n} x_{i}^{2} + \sum_{i = 1}^{n} y_{i}^{2}) / 2}, and where$ $V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 19. The system of claim 14, wherein the similarity metric is computed according to:
    - $extended jaccard similarity metric = \frac{V_{1} \cdot V_{2}}{(\sum_{i = 1}^{n} x_{i}^{2} + \sum_{i = 1}^{n} y_{i}^{2}) - (V_{1} \cdot V_{2})}, where$ $V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 20. The system of claim 14, wherein the similarity metric is computed according to:
    - $z - method similarity metric = \sum_{i = 1}^{n} (a_{i} + b_{i}) \times c_{i}, where$ $a_{i} = \max (x_{i}, y_{i}), b_{i} = \min (x_{i}, y_{i}), c_{i} = \frac{b_{i}}{a_{i}},$ and x, y are information values.
  - 21. The system of claim 20, wherein the similarity metric is further computed according to:
    - if (a_i+b_i)×
      
      c_i≧
      
      a_i, then substitute b_ifor (a_i+b_i)×
      
      c_i.
  - 22. The system of claim 20, wherein the similarity metric is further computed according to:
    - if (a_i+b_i)×
      
      c_i≧
      
      a_i, then substitute $\frac{(a_{i} + b_{i})}{2}$ for (a_i+b_i)×
      
      c_i.
  - 23. The system of claim 14, wherein the similarity metric is computed according to:
    - $L_{1} norm similarity metric = \frac{1}{2} L_{1} (p, q), wherein$ $L_{1} = \sum_{i} \langle p_{i} - q_{i} \rangle,$ and wherein p, q are conditional probabilities of information values between two semantic profiles computed as an information value for each semcode in a profile divided by a sum of information values for that profile.
  - 24. The system of claim 14, wherein the similarity metric is computed according to:
    - similarity metric=iv_sc,d×
      
      idv_sc, wherein iv_sc,dis an information value of a semcode sc in a document d, and idv_scis a value that a semcode sc has across an entire collection.

25. A computer program product for determining similarity between portions of text comprising:
- a computer readable storage medium;
  
  computer program instructions, recorded on the computer readable storage medium, executable by a processor, for performing the steps of generating a semantic profile for at least two portions of text, each semantic profile comprising a vector of values; and
  
  computing a similarity metric representing a similarity between the at least two portions of text using the at least two generated semantic profiles.
- View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)
- - 26. The computer program product of claim 25, wherein the semantic profile comprising a vector of information values.
  - 27. The computer program product of claim 26, wherein a portion of text comprises a sentence.
  - 28. The computer program product of claim 26, wherein a portion of text comprises a paragraph.
  - 29. The computer program product of claim 26, wherein the similarity metric is computed according to:
    - $extended cosine similarity metric = \frac{V_{1} \cdot V_{2}}{ V_{1}  \times  V_{2} }, where$ $ V  = \sqrt{\sum_{i = 1}^{n} x^{2}}, V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 30. The computer program product of claim 26, wherein t the similarity metric is computed according to:
    - $extended dice similarity metric = \frac{V_{1} \cdot V_{2}}{(\sum_{i = 1}^{n} x_{i}^{2} + \sum_{i = 1}^{n} y_{i}^{2}) / 2}, and where V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 31. The computer program product of claim 26, wherein the similarity metric is computed according to:
    - $extended jaccard similarity metric = \frac{V_{1} \cdot V_{2}}{(\sum_{i = 1}^{n} x_{i}^{2} + \sum_{i = 1}^{n} y_{i}^{2}) - (V_{1} \cdot V_{2})}, where V_{1} \cdot V_{2} = \sum_{i = 1}^{n} x_{i} y_{i},$ and x,y are information values of segments.
  - 32. The computer program product of claim 26, wherein the similarity metric is computed according to:
    - $z - method similarity metric = \sum_{i = 1}^{n} (a_{i} + b_{i}) \times c_{i}, where a_{i} = \max (x_{i}, y_{i}), b_{i} = \min (x_{i}, y_{i}), c_{i} = \frac{b_{i}}{a_{i}},$ and x, y are information values.
  - 33. The computer program product of claim 32, wherein the similarity metric is further computed according to:
    - if (a_i+b_i)×
      
      c_i≧
      
      a_i, then substitute b_ifor (a_i+b_i)×
      
      c_i.
  - 34. The computer program product of claim 32, wherein the similarity metric is further computed according to:
    - if (a_i+b_i)×
      
      c_i≧
      
      a_i, then substitute $\frac{(a_{i} + b_{i})}{2}$ for (a_i+b_i)×
      
      c_i.
  - 35. The computer program product of claim 26, wherein the similarity metric is computed according to:
    - $L_{1} norm similarity metric = \frac{1}{2} L_{1} (p, q), wherein L_{1} = \sum_{i} \langle p_{i} - q_{i} \rangle,$ and wherein p, q are conditional probabilities of information values between two semantic profiles computed as an information value for each semcode in a profile divided by a sum of information values for that profile.
  - 36. The computer program product of claim 26, wherein the similarity metric is computed according to:
    - similarity metric=iv_sc,d×
      
      idv_sc, wherein iv_sc,dis an information value of a semcode sc in a document d, and idv_scis a value that a semcode sc has across an entire collection.

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Current Assignee
Applied Linguistics LLC
Original Assignee
Applied Linguistics LLC
Inventors
Timofeyev, Maksim, Speers, d'Armond, Scott, Bernard

Application Number

US11/443,213
Publication Number

US 20070073745A1
Time in Patent Office

Days
Field of Search
US Class Current

1/1
CPC Class Codes

G06F 16/374 Thesaurus

Similarity metric for semantic profiling

First Claim

1 Assignment

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Abstract

357 Citations

36 Claims

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Similarity metric for semantic profiling

First Claim

1 Assignment

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0 Petitions

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Accused Products

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Abstract

357 Citations

36 Claims

Specification

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Solutions

Use Cases

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