Triangulated natural language decoding from forecasted deep semantic representations
Computer-implemented method includes developing, via a processor, a words model from a plurality of natural language text based articles relating to a subject and generating, via the processor, a static vector based upon the words model. The computer-implemented method further includes developing, via the processor, an actual articles model from actual articles, generating, via the processor, a bootstrapped vector using the actual articles model, generating, via the processor, a n-dimensional depth item using the static vector and the bootstrapped vector, and determining, via the processor, evidence based on the n-dimensional depth item. The computer-implemented method still further includes presenting, via the processor and a display, the evidence base upon an input query from a user.
1. A computer-implemented method comprising:
developing, via a processor, a words model from a plurality of natural language text based articles relating to a subject;
generating, via the processor, a static vector based upon the words model;
developing, via the processor, an actual articles model from actual articles;
generating, via the processor, a bootstrapped vector using the actual articles model;
generating, via the processor, a n-dimensional depth item using the static vector and the bootstrapped vector;
determining, via the processor, evidence based on the n-dimensional depth item;
generating, via the processor, a forecast vector based upon historical articles; and
presenting, via the processor and a display, the evidence base upon an input query from a user.
2. The computer-implemented method according to claim 1 , further comprising modifying, via the processor, the n-dimensional depth item using the forecast vector.
3. The computer-implemented method according to claim 1 , wherein the presented evidence determines how keywords are trending over time.
4. The computer-implemented method according to claim 1 , wherein the n-dimensional depth item forms a geographic boundary.
5. The computer-implemented method according to claim 4 , further comprising generating, via the processor, a forecast vector based upon historical articles.
6. The computer-implemented method according to claim 5 , wherein the forecast vector, the static vector and the bootstrapped vector form a triangle area, such that a lowest triangle area produces better evidence for the user.
7. The computer-implemented method according to claim 1 , wherein the presented evidence determines how concepts are trending over time.
8. The computer-implemented method according to claim 1 , wherein the presented evidence determines how entities are trending over time.
9. A system comprising:
a memory having computer readable instructions; and
one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:
developing a words model from a plurality of natural language text based articles relating to a subject;
generating a static vector based upon the words model;
developing an actual articles model from actual articles;
generating a bootstrapped vector using the actual articles model;
generating a n-dimensional depth item using the static vector and the bootstrapped vector;
determining evidence based on the n-dimensional depth item;
generating a forecast vector based upon historical articles; and
presenting, via a display, the evidence base upon an input query from a user.
10. The system according to claim 9 , further comprising modifying the n-dimensional depth item using the forecast vector.
11. The system according to claim 9 , wherein the presented evidence determines how keywords are trending over time.
12. The system according to claim 9 , wherein the n-dimensional depth item forms a geographic boundary.
13. The system according to claim 12 , further comprising generating a forecast vector based upon historical articles.
14. The system according to claim 13 , wherein the forecast vector, the static vector and the bootstrapped vector form a triangle area, such that a lowest triangle area produces better evidence for the user.
15. The system according to claim 9 , wherein the presented evidence determines how concepts are trending over time.
16. The system according to claim 9 , wherein the presented evidence determines how entities are trending over time.
17. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:
developing a words model from a plurality of natural language text based articles relating to a subject;
generating a static vector based upon the words model;
developing an actual articles model from actual articles;
generating a bootstrapped vector using the actual articles model;
generating a n-dimensional depth item using the static vector and the bootstrapped vector;
determining evidence based on the n-dimensional depth item;
generating a forecast vector based upon historical articles; and
presenting, via a display, the evidence base upon an input query from a user.
18. The computer program product according to claim 17 , wherein the n-dimensional depth item forms a geographic boundary and the forecast vector, the static vector and the bootstrapped vector form a triangle area, such that a lowest triangle area produces better evidence for the user.