Claims:WE CLAIM
1. A method of creating a concept map for a text corpus, the method comprising:
extracting, by a computing device, a plurality of n-grams from the text corpus;
creating, by the computing device, a gravity matrix based on a frequency of occurrence of each of the plurality of n-grams within the text corpus and word-distance amongst the plurality of n-grams;
calculating, by the computing device, a corpus gravity based on the gravity matrix, the corpus gravity being an aggregate of sum of each row or each column in the gravity matrix;
determining, by the computing device, a concept gravity and a concept influence for each of the plurality of n-grams in the gravity matrix based on the corpus gravity, a row aggregate associated with each of the plurality of n-grams in the gravity matrix, and a column aggregate associated with each of the plurality of n-grams in the gravity matrix; and
creating, by the computing device, the concept map based on the concept gravity and the concept influence determined for each of the plurality of n-grams.
2. The method of claim 1 further comprising determining the frequency of occurrence of each of the plurality of n-grams within the text corpus.
3. The method of claim 1 further comprising computing at least one word-distance between two n-grams selected from the plurality of n-grams.
4. The method of claim 3, wherein a first word-distance of the at least one word-distance is equal to number of words between occurrence of a first n-gram of the two n-grams followed by occurrence of a second n-gram of the two n-grams.
5. The method of claim 4, wherein a second word-distance of the at least one word-distance is equal to number of words between occurrence of the second n-gram followed by occurrence of the first n-gram.
6. The method of claim 3, wherein value of an element in the gravity matrix corresponding to intersection of the two n-grams is computed based on a frequency of occurrence of each of the two n-grams and one of the at least one word-distance between two n-grams.
7. The method of claim 1 further comprising performing a plurality of data cleansing operations on the text corpus, the plurality of n-grams being extracted subsequent to performing the plurality of data cleansing operations.
8. The method of claim 1, wherein the gravity matrix comprises a subset of the plurality of n-grams, frequency of occurrence of each n-gram in the subset being greater than a predefined frequency of occurrence.
9. The method of claim 1, wherein a concept gravity for an n-gram in the gravity matrix is determined based on the corpus gravity and a row sum associated with the n-gram in the gravity matrix.
10. The method of claim 1, wherein a corpus influence for an n-gram in the gravity matrix is determined based on the corpus gravity and a column sum associated with the n-gram in the gravity matrix.
11. A computing device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor, wherein the memory stores processor instructions, which, on execution, causes the at least one processor to:
extract a plurality of n-grams from the text corpus;
create a gravity matrix based on a frequency of occurrence of each of the plurality of n-grams within the text corpus and word-distance amongst the plurality of n-grams;
calculate a corpus gravity based on the gravity matrix, the corpus gravity being an aggregate of sum of each row or each column in the gravity matrix;
determine a concept gravity and a corpus influence for each of the plurality of n-grams in the gravity matrix based on the corpus gravity, a row aggregate associated with each of the plurality of n-grams in the gravity matrix, and a column aggregate associated with each of the plurality of n-grams in the gravity matrix; and
create the concept map based on the concept gravity and the corpus influence determined for each of the plurality of n-grams.
12. The computing device of claim 1, wherein the at least one processor is further configured to determine the frequency of occurrence of each of the plurality of n-grams within the text corpus.
13. The computing device of claim 1, wherein the at least one processor is further configured to compute at least one word-distance between two n-grams selected from the plurality of n-grams.
14. The computing device of claim 13, wherein a first word-distance of the at least one word-distance is equal to number of words between occurrence of a first n-gram of the two n-grams followed by occurrence of a second n-gram of the two n-grams.
15. The computing device of claim 14, wherein a second word-distance of the at least one word-distance is equal to number of words between occurrence of the second n-gram followed by occurrence of the first n-gram.
16. The computing device of claim 13, wherein value of an element in the gravity matrix corresponding to intersection of the two n-grams is computed based on a frequency of occurrence of each of the two n-grams and one of the at least one word-distance between two n-grams.
17. The computing device of claim 11, wherein the gravity matrix comprises a subset of the plurality of n-grams, frequency of occurrence of each n-gram in the subset being greater than a predefined frequency of occurrence.
18. The computing device of claim 1, wherein a concept gravity for an n-gram in the gravity matrix is determined based on the corpus gravity and a row aggregate associated with the n-gram in the gravity matrix.
19. The computing device of claim 1, wherein a corpus influence for an n-gram in the gravity matrix is determined based on the corpus gravity and a column aggregate associated with the n-gram in the gravity matrix.

Dated this 06th day of January 2017

Swetha SN
Of K&S Partner
Agent for the Applicant , Description:TECHNICAL FIELD
This disclosure relates generally to concept maps and more particularly to systems and methods for creating concept maps using concept gravity matrix.