Assume the task here is extracting important facts for resume like a candidate skills and his education etc.., Here is resume is parsed text from pdf or docx resume.
First, I'll obtain skills and education data from various online websites, job portals etc.., the obtaining data will be two text files and in each file every row represent a skills or a university name like below,
skills.txt ___________ c python java node js education.txt ___________ massachusetts institute of Technology harvard university
I want to know if these be enough to be able to train a named entity recognition model to recognize skills and education for raw resume text. The data I have is not sentences but just entities. I've read somewhere that we require some context along with the entity for NER model to learn better. Like this example below,
skills.txt ___________ c is used at facebook python is my favorite programming language
If I use my collected data education.txt to train the modeel and BIO annotate them then it will be like below, it won't have O-Other token.
massachusetts B-EDU institute I-EDU of I-EDU Technology I-EDU harvard B-EDU university I-EDU indian B-EDU institute I-EDU of I-EDU technology I-EDU
But I don't know how to access such data for my resume-extraction problem. How do I proceed further? How to build an effective NER model for my resume facts identification domain-specific task? Any inputs/suggestions would really help.