Resume Parser With Natural Language Processing

Resume Parser With Natural Language Processing – When I’m still at university, I’m curious about how automatic resume information extraction works. I will prepare various formats of my resumes and upload them to the job portal to test how the algorithm actually works. I’ve always wanted to build one myself. Thus, during the last weeks of my free time, I decided to create a resume parser.

At first I thought it was pretty simple. Just use some templates to extract information, but it turns out I’m wrong! Creating a resume parser is hard, there are so many kinds of resume layouts you can imagine.

Resume Parser With Natural Language Processing

Resume Parser With Natural Language Processing

For example, some people put the date before their resume title, some people don’t include the length of their work experience, and some people don’t list the company on their resume. This further complicates the creation of a resume parser, as there are no patch patterns to commit.

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After one month of work, based on my experience, I would like to share which methods work well and what you should pay attention to before you start creating your own resume parser.

Before going into details, here is a short video showing the end result of the resume parser.

One of the challenges of data collection is finding a good source for a resume. Once you can detect it, the cleanup part will be ok if you don’t hit the server too often.

After that, I selected several resumes and manually labeled the data in each field. The tagging work is done so that I can compare the performance of different parsing methods.

Online Resume Parsing System Using Text Analytics

For the rest, I use Python. There are several packages for converting PDF formats to text such as PDF Miner, Apache Tika, pdftotree, etc. Let me make some comparisons between different text extraction methods.

One of the disadvantages of using PDF Miner is that you are dealing with a resume that is similar to the Linkedin resume format as shown below.

PDF Miner reads PDF line by line. Thus, text from the left and right sides will be merged together if they happen to be on the same line. Therefore, as you might imagine, it will be more difficult for you to extract information in subsequent steps.

Resume Parser With Natural Language Processing

On the other hand, pdftree will skip all “n” characters, so the extracted text will be something like a piece of text. Thus, it is difficult to divide them into several sections.

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So I use the Apache Tika tool which seems to be the best option for parsing PDF files, while for docx files I use the parsing docx package.

Here’s the tricky part. There are several ways to deal with this, but I will share with you the best ways I have found and the basic method.

Let’s talk about the base method first. The basic method I use is to first scrape the keywords for each section (the sections I’m referring to here).

For example, I want to extract the name of the university. So I first find the website that lists most of the universities and clean them up. I then use a regular expression to see if that university name can be found on a particular resume. If found, this piece of information will be extracted from the resume.

Resumes · Github Topics · Github

This way I can create a base method that I will use to compare the performance of my other parsing method.

. What I do is have a set of keywords for the title of each main section, like so,

Of course, you can try to build a machine learning model that will perform the division, but I chose the easiest way.

Resume Parser With Natural Language Processing

After that, there will be a separate script to process each main section separately. Each script will define its own rules that use the cleaned data to extract information for each field. The rules in each script are actually quite messy and complex. Since I would like to keep this article as simple as possible, I will not cover it now. If you are interested to know the details, write in the comments!

Why You Need A Resume Parser Using Nlp

. The reason I’m using a machine learning model here is because I’ve found some obvious patterns to distinguish between a company name and a job title, for example, when you see the keywords “Private Limited” or “Pte Ltd”, are you sure what is the name of the company.

I copied the data from the green paper to get the company names and downloaded the job titles from this Github repository.

With the data in hand, I just trained a very simple naive bayes model that could improve job classification accuracy by at least 10%.

The reason I use token_set_ratio is that if the parsed result has more tokens in common with the labeled result, it means the parser performance is better.

Natural Language Processing

If you have other ideas for performance metrics, feel free to leave a comment below!

Thank you very much for reading to the end. This project is really taking up a lot of my time. However, if you want to solve some tricky problems, you can try this project! 🙂

Low Wei Hong is a Data Scientist at Shopee. His experience has more included scanning websites, building a data pipeline, and implementing machine learning models to solve business problems.

Resume Parser With Natural Language Processing

It provides scanning services that can provide you with the accurate and cleaned data you need. You can visit this website to view his portfolio and also contact him for scanning services.

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Low Wei Hong – MediumRead Low Wei Hong’s entries on Medium. Data Scientist | Web scraping service: https://www.thedataknight.com/. Every… medium.com Use the popular Spacy NLP Python library for optical character recognition and text classification to create a Python resume parser.

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The 8 Best Resume Parsing Software

Imagine that you are an intern in the human resources department of a company and you have been given a huge pile of approximately 1,000 resumes. Your task is to prepare a list of candidates suitable for the role of a software engineer. Now, since this company didn’t provide candidates with a resume format, it’s your job to review each resume by hand. How tiring, right? Well, there is an easy way out: create a resume analysis application that takes a resume as input and then extracts and analyzes all the valuable information from it. It is not easy for company recruiters and HR teams to scan thousands of qualified resumes. They either need a lot of people to do this, or miss out on suitable candidates. Spending too much work time manually sorting candidates’ resumes is a waste of time, money, and company productivity. Therefore, we suggest you work on this resume analysis project, which can automate the separation task and save companies a lot of time.

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