SCORING MODEL METHODS AND APPARATUS

1 . A method, comprising:

using at least one computer hardware processor to perform:

obtaining information associated with a job;

obtaining semi-structured natural language input comprising credentials of a candidate for the job;

identifying the candidate's credentials at least in part by automatically processing the semi-structured natural language input; and

calculating a talent score for the candidate based, at least in part, on the identified the candidate's credentials and the information associated with the job.

2 . The method of claim 1 , wherein automatically processing the semi-structured natural language input comprises:

identifying a first portion of the semi-structured natural language input comprising information about a first type of credential of the candidate; and

identifying a second portion of the semi-structured natural language input comprising information about a second type of credential of the candidate,

wherein the first type of credential is different from the second type of credential.

3 . The method of claim 1 , wherein:

identifying the first portion of the semi-structured natural language input comprises identifying a portion of the semi-structured natural language input comprising information about the candidate's academic credentials; and

identifying the second portion of the semi-structured natural language input comprises identifying a portion of the semi-structured natural language input comprising information about the candidate's professional credentials.

4 . The method of claim 3 , further comprising processing the first portion using at least one natural language processing technique to identify a school the candidate attended, the candidate's major at the school, and/or the candidate's GPA.

5 . The method of claim 4 , wherein the at least one natural language processing technique comprising a keyword matching technique.

6 . The method of claim 4 , wherein processing the first portion to identify the school the candidate attended is performed at least in part by using information indicating a plurality of names for the school, each of the plurality of names identifying the school.

7 . The method of claim 1 , wherein the semi-structured natural language input comprises a resume of the candidate.

8 . A system, comprising:

at least one computer hardware processor programmed to perform:

obtaining information associated with a job;

obtaining semi-structured natural language input comprising credentials of a candidate for the job;

identifying the candidate's credentials at least in part by automatically processing the semi-structured natural language input; and

calculating a talent score for the candidate based, at least in part, on the identified the candidate's credentials and the information associated with the job.

9 . The system of claim 8 , wherein automatically processing the semi-structured natural language input comprises:

identifying a first portion of the semi-structured natural language input comprising information about a first type of credential of the candidate; and

identifying a second portion of the semi-structured natural language input comprising information about a second type of credential of the candidate,

wherein the first type of credential is different from the second type of credential.

10 . The system of claim 8 , wherein:

identifying the first portion of the semi-structured natural language input comprises identifying a portion of the semi-structured natural language input comprising information about the candidate's academic credentials; and

identifying the second portion of the semi-structured natural language input comprises identifying a portion of the semi-structured natural language input comprising information about the candidate's professional credentials.

11 . The system of claim 10 , further comprising processing the first portion using at least one natural language processing technique to identify a school the candidate attended, the candidate's major at the school, and/or the candidate's GPA.

12 . The system of claim 10 , wherein the at least one natural language processing technique comprising a keyword matching technique.

13 . The system of claim 11 , wherein processing the first portion to identify the school the candidate attended is performed at least in part by using information indicating a plurality of names for the school, each of the plurality of names identifying the school.

14 . The system of claim 8 , wherein the semi-structured natural language input comprises a resume of the candidate.

15 . At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at last one computer hardware processor to perform a method comprising:

obtaining information associated with a job;

obtaining semi-structured natural language input comprising credentials of a candidate for the job;

identifying the candidate's credentials at least in part by automatically processing the semi-structured natural language input; and

calculating a talent score for the candidate based, at least in part, on the identified the candidate's credentials and the information associated with the job.

16 . The at least one non-transitory computer-readable storage medium of claim 15 , wherein automatically processing the semi-structured natural language input comprises:

identifying a first portion of the semi-structured natural language input comprising information about a first type of credential of the candidate; and

identifying a second portion of the semi-structured natural language input comprising information about a second type of credential of the candidate,

wherein the first type of credential is different from the second type of credential.

17 . The at least one non-transitory computer-readable storage medium of claim 15 , wherein:

identifying the first portion of the semi-structured natural language input comprises identifying a portion of the semi-structured natural language input comprising information about the candidate's academic credentials; and

identifying the second portion of the semi-structured natural language input comprises identifying a portion of the semi-structured natural language input comprising information about the candidate's professional credentials.

18 . The at least one non-transitory computer-readable storage medium of claim 17 , further comprising processing the first portion using at least one natural language processing technique to identify a school the candidate attended, the candidate's major at the school, and/or the candidate's GPA.

19 . The at least one non-transitory computer-readable storage medium of claim 18 , wherein processing the first portion to identify the school the candidate attended is performed at least in part by using information indicating a plurality of names for the school, each of the plurality of names identifying the school.

20 . The at least one non-transitory computer-readable storage medium of claim 15 , wherein the semi-structured natural language input comprises a resume of the candidate.