SYSTEMS AND METHODS TO DEVELOP TRAINING SET OF DATA BASED ON RESUME CORPUS

• US 20170286914A1
• Filed: 04/05/2016
• Published: 10/05/2017
• Est. Priority Date: 04/05/2016
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Fang, Miaoqing
• Granted Patent: US 10,748,118 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/105 Human resources
• CPC Class Codes: G06Q 10/1053 Employment or hiring

Abstract

Systems, methods, and non-transitory computer readable media are configured to acquire a resume corpus. The resume corpus is processed to generate resume tokens. A machine learning model is trained based on the resume tokens. The machine learning model is applied to recommend a job classification based on evaluation data.

Claims

1. A computer-implemented method comprising:

acquiring, by a computing system, a resume corpus;

processing, by the computing system, the resume corpus to generate resume tokens;

training, by the computing system, a machine learning model based on the resume tokens; and

applying, by the computing system, the machine learning model to recommend a job classification based on evaluation data.

2. The computer-implemented method of claim 1, wherein the resume corpus is based on textual data from a plurality of resumes.

3. The computer-implemented method of claim 1, wherein the resume tokens include one or more unigrams and one or more bigrams.

4. The computer-implemented method of claim 1, wherein the processing the resume corpus comprises:

removing stop words from the resume corpus.

5. The computer-implemented method of claim 4, wherein the stop words include at least one of pronouns, prepositions, articles, and conjunctions.

6. The computer-implemented method of claim 1, wherein the processing the resume corpus comprises:

modifying capitalized letters of words in the resume corpus to have lowercase letters.

7. The computer-implemented method of claim 1, wherein the processing the resume corpus comprises:

determining a value based on a number of instances in which a first word and a second word co-occur and a number of instances in which the first word and the second word occur individually.

8. The computer-implemented method of claim 1, wherein the processing the resume corpus comprises:

generating a whitelist of bigrams constituting job titles parsed from the resume corpus.

9. The computer-implemented method of claim 8, wherein the processing the resume corpus further comprises:

including a bigram in the whitelist based on satisfaction of a threshold appearance value relating to the bigram.

10. The computer-implemented method of claim 1, wherein the job classification includes a job title or a job pipeline.

11. A system comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, cause the system to perform;

acquiring a resume corpus;

processing the resume corpus to generate resume tokens;

training a machine learning model based on the resume tokens; and

applying the machine learning model to recommend a job classification based on evaluation data.

12. The system of claim 11, wherein the resume corpus is based on textual data from a plurality of resumes.

13. The system of claim 11, wherein the resume tokens include one or more unigrams and one or more bigrams.

14. The system of claim 11, wherein the processing the resume corpus comprises:

removing stop words from the resume corpus.

15. The system of claim 14, wherein the stop words include at least one of pronouns, prepositions, articles, and conjunctions.

16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:

acquiring a resume corpus;

processing the resume corpus to generate resume tokens;

training a machine learning model based on the resume tokens; and

applying the machine learning model to recommend a job classification based on evaluation data.

17. The non-transitory computer-readable storage medium of claim 16, wherein the resume corpus is based on textual data from a plurality of resumes.

18. The non-transitory computer-readable storage medium of claim 16, wherein the resume tokens include one or more unigrams and one or more bigrams.

19. The non-transitory computer-readable storage medium of claim 16, wherein the processing the resume corpus comprises:

removing stop words from the resume corpus.

20. The non-transitory computer-readable storage medium of claim 19, wherein the stop words include at least one of pronouns, prepositions, articles, and conjunctions.