Systems and methods to develop training set of data based on resume corpus
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.
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 from the resume corpus, wherein the processing comprises:
determining a ratio based on co-occurrence of a first word and a second word of the resume corpus versus individual occurrence of the first word and the second word; and
determining, based on the ratio, the existence of a bigram including the first word and the second word to be used as training data;
training, by the computing system, a machine learning model to recommend a job classification based at least in part on the bigram; 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:
generating a whitelist of bigrams constituting job titles parsed from the resume corpus,
wherein the training a machine learning model to recommend a job classification is further based at least in part on the whitelist of bigrams constituting job titles.
8. The computer-implemented method of claim 7 , 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.
9. The computer-implemented method of claim 1 , wherein the job classification includes a job title or a job pipeline.
10. The computer-implemented method of claim 1 , wherein the processing further comprises:
comparing the ratio to a threshold value; and
determining the existence of a bigram including the first word and the second word to be used as training data when the ratio satisfies the threshold value.
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 from the resume corpus, wherein the processing comprises:
determining a ratio based on co-occurrence of a first word and a second word of the resume corpus versus individual occurrence of the first word and the second word; and
determining, based on the ratio, the existence of a bigram including the first word and the second word to be used as training data;
training a machine learning model to recommend a job classification based at least in part on the bigram; 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 from the resume corpus, wherein the processing comprises:
determining a ratio based on co-occurrence of a first word and a second word of the resume corpus versus individual occurrence of the first word and the second word; and
determining, based on the ratio, the existence of a bigram including the first word and the second word to be used as training data;
training a machine learning model to recommend a job classification based at least in part on the bigram; 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.