Method and system for document similarity analysis based on common denominator similarity
A method for document similarity analysis. The method includes obtaining a document to be archived, and identifying a document category similar to the document to be archived. The similar document category is identified by: identifying a document category that includes indexing terms that are identical to indexing terms in the document to be archived, obtaining term frequency vectors for the identical indexing terms in the document to be archived and in the identified document category, generating normalized term frequency vectors, from the term frequency vectors, calculating a common denominator similarity based on the normalized term frequency vectors and a common denominator, and determining that the document category is similar to the document to be archived based on the common denominator similarity. The method further includes registering the document to be archived in the document category.
1. A method for document similarity analysis, the method comprising:
obtaining a document to be archived;
identifying a document category similar to the document to be archived, based on indexing terms and corresponding term frequencies, comprising:
identifying a document category that includes a plurality of indexing terms that are identical to indexing terms identified in the document to be archived;
obtaining a term frequency vector for the identical indexing terms in the document to be archived;
generating a normalized term frequency vector, from the term frequency vector for the document to be archived;
obtaining a term frequency vector for the identical indexing terms in the identified document category;
generating a normalized term frequency vector, from the term frequency vector for the identified document category;
calculating a common denominator similarity based on the normalized term frequency vector for the document to be archived, the normalized term frequency vector for the identified document category, and a common denominator;
making a determination that the document category is similar to the document to be archived based on the common denominator similarity; and
registering the document to be archived in the document category.
2. The method of claim 1 , further comprising:
obtaining a target document;
obtaining indexing terms for the target document;
identifying document categories similar to the target document, based on indexing terms and corresponding term frequencies of the target document and of the document categories;
identifying, in the identified document categories, at least one similar document; and
returning the at least one similar document.
3. The method of claim 1 , wherein the term frequency vector for the document to be archived specifies frequencies of the identical indexing terms in the document to be archived.
4. The method of claim 1 , wherein the normalized term frequency vector is obtained from the term frequency vector by applying the common denominator to vector elements of the term frequency vector.
5. The method of claim 1 , wherein calculating the common denominator similarity comprises:
for each pair of a vector element of the normalized term frequency vector for the document to be archived and a corresponding vector element of the normalized term frequency vector for the identified document category: identifying the smaller vector element; and
applying the common denominator to a vector composed of the identified smaller vector elements.
6. The method of claim 1 , further comprising:
identifying a second document category similar to the document to be archived; and
registering the document in the second document category.
7. The method of claim 1 , further comprising:
making a second determination that the similarity between the document to be archived and the document category is weak, and based on the second determination:
generating a new document category and registering the document to be archived in the new document category.
8. The method of claim 7 , wherein a common denominator similarity of at least 0.4 indicates that the document category is at least weakly similar to the document to be archived.
9. The method of claim 7 , wherein generating a new document category comprises:
assigning the indexing terms and the term frequencies for the document to be archived to the new document category.
10. The method of claim 1 , further comprising:
obtaining a second document to be archived;
obtaining indexing terms for the second document to be archived;
identifying document categories similar to the second document to be archived, based on indexing terms and corresponding term frequencies for the second document to be archived; and
making a second determination that a highly similar document category exists and based on the second determination: registering the document to be archived with the highly similar document category.
11. The method of claim 10 , wherein a common denominator similarity between the highly similar document category and the document to be archived is at least 0.7.
12. A non-transitory computer readable medium (CRM) comprising instructions that enable a system for document similarity analysis to:
obtain a document to be archived;
identify a document category similar to the document to be archived, based on index terms and corresponding term frequencies, comprising:
identifying a document category that includes a plurality of indexing terms that are identical to indexing terms identified in the document to be archived;
obtaining a term frequency vector for the identical indexing terms in the document to be archived;
generating a normalized term frequency vector, from the term frequency vector for the document to be archived;
obtaining a term frequency vector for the identical indexing terms in the identified document category;
generating a normalized term frequency vector, from the term frequency vector for the identified document category;
calculating a common denominator similarity based on the normalized term frequency vector for the document to be archived, the normalized term frequency vector for the identified document category, and a common denominator;
making a determination that the document category is similar to the document to be archived based on the common denominator similarity; and
register the document to be archived in the document category.
13. The non-transitory CRM of claim 12 , wherein the instructions further enable the system for document similarity analysis to:
obtain a target document;
obtain indexing terms for the target document;
identify document categories similar to the target document, based on indexing terms and corresponding term frequencies of the target document and of the document categories;
identify, in the identified document categories, at least one similar document; and
return the at least one similar document.
14. The non-transitory CRM of claim 12 , wherein the normalized term frequency vector is obtained from the term frequency vector by applying the common denominator to vector elements of the term frequency vector.
15. The non-transitory CRM of claim 12 , wherein calculating the common denominator similarity comprises:
for each pair of a vector element of the normalized term frequency vector for the document to be archived and a corresponding vector element of the normalized term frequency vector for the identified document category: identifying the smaller vector element; and
applying the common denominator to a vector composed of the identified smaller vector elements.
16. The non-transitory CRM of claim 12 , wherein the instructions further enable the system for document similarity analysis to:
identify a second document category similar to the document to be archived; and
register the document in the second document category.
17. A system for document similarity analysis, the system comprising:
a document categorization and search engine; and
a document repository;
wherein the document categorization and search engine:
obtains a document to be archived;
identifies, in the document repository, a document category similar to the document to be archived, based on indexing terms and corresponding term frequencies, comprising:
identifying a document category that includes a plurality of indexing terms that are identical to indexing terms identified in the document to be archived;
obtaining a term frequency vector for the identical indexing terms in the document to be archived;
generating a normalized term frequency vector, from the term frequency vector for the document to be archived;
obtaining a term frequency vector for the identical indexing terms in the identified document category;
generating a normalized term frequency vector, from the term frequency vector for the identified document category;
calculating a common denominator similarity based on the normalized term frequency vector for the document to be archived, the normalized term frequency vector for the identified document category, and a common denominator;
making a determination that the document category is similar to the document to be archived based on the common denominator similarity; and
registers the document to be archived in the document category.
18. The system of claim 17 , wherein the document categorization and search engine further:
obtains a target document;
obtains indexing terms for the target document;
identifies document categories similar to the target document, based on indexing terms and corresponding term frequencies of the target document and of the document categories;
identifies, in the identified document categories, at least one similar document; and
returns the at least one similar document.
19. The system of claim 17 , wherein the normalized term frequency vector is obtained from the term frequency vector by applying the common denominator to vector elements of the term frequency vector.
20. The system of claim 17 , wherein calculating the common denominator similarity comprises:
for each pair of a vector element of the normalized term frequency vector for the document to be archived and a corresponding vector element of the normalized term frequency vector for the identified document category: identifying the smaller vector element; and
applying the common denominator to a vector composed of the identified smaller vector elements.