# How to prepare data for Named Entity Recognition with BIO annotation?

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
___________
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.

• Actually my input text is just like in first two files skills.txt and education.txt and I want to train the model using that. But I'm not sure if it works or not because it doesn't have any context information around it. So, my question, is it possible to train a resume extraction NER model using the combination of those two files as input and train it to learn entities then inference it on raw resume text. – user_12 May 14 at 16:22