A resume is very important in terms of getting started in business today. Having a professional resume when applying for a job makes us stand out among other candidates.You might think that many of us are reviewing resumes one by one, postings to job leads by recruiters. But this is not how it works today. What many job seekers don’t realize is that 75 percent of job applications are rejected before they are seen by a human.
Before your resume reaches the hands of a living person, it’s often scanned by what’s known as an applicant tracking system (ATS). So what is this ATS and how does it work, let’s take a look at it.
Table of Contents
What is Applicant Tracking System?
Applicant Tracking System (ATS) is a program that performs resume pre-screening and filtering by removing the human factor. While its positive reflection is a screening method free from prejudices, its disadvantage is that it also eliminates resumes that are not ATS compatible but suitable for job postings. ATS programs perform the resume parsing process, which we aim to address in this article.
What is Resume Parsing?
A resume parser is a software that can read, understand and then classify all the data in the resume like a human can. Resume parsing basically extracts data from a resume. So it is a resume scraper. The most important reason why resume parser is preferred is that it works faster than humans.
Many businesses today have written their own resume parsing applications. In this way, businesses can filter and shape their resume parsing applications as they wish. However, the most popular way to resume parsing today is to use the resume parser APIs. Resume parser APIs are basically web services that parse and return a resume in a specific format. Resume parser APIs return resume parsing information in a format, while classifying and categorizing the data it returns. In this way, it provides an output that is easy to process and analyze.
There are many resume parser APIs in the market for the Resume parsing process. Today, the most preferred resume parsing API by developers and businesses is the Resume Parser API offered under Apilayer.
How Does Resume Parsing Work?
The most important reasons why Resume Parser API is the most popular resume parsing API is that it works fast and the accuracy of the data it provides is high. These come up on how the resume parser API works. Resume Parser API uses NLP algorithms that continuously improve itself in order to ensure the accuracy of the data it will provide while performing the resume converter process.
Today, NLP algorithms used by companies such as Google, Twitter and Microsoft aim to understand or reproduce the canonical structure of natural languages by analyzing them. The convenience that this analysis will bring to people can be summarized with many topics such as automatic translation of written documents, question-answer machines, automatic speech and command comprehension, speech synthesis, speech generation, automatic text summarization, and information provision. The main reason for the high accuracy of the data provided by the Resume Parser API is the NLP algorithms. Resume parser APIs have faster results than their competitors in resume parsing operations. The main reason for this is that NLP algorithms on the working principle are tested with the highest technological infrastructure.
Why Should Businesses Use the Resume Parser API?
There are many reasons to use the Resume parser API in your businesses. Let’s list some of them as follows.
Time Saving
If your businesses have 10 to 20 resumes to review or even analyze, you can quickly do this manually. But what if you need to quickly moan and filter thousands or even hundreds of thousands of resumes? Just in this case, using the resume parser API, you can parse as many resumes as you want in just seconds and categorize them. In particular, it is very important that the dataset fed to the algorithm trained in artificial intelligence applications is large and full of up-to-date information. You may need to add thousands of resumes to your dataset every day. At this stage, you can automate the resume parser API to provide a steady stream of data from target resumes to your reverset.
Having Your Own ATS Application
There are many different resume parsing ATS software available on the market, and the features offered will differ depending on which brand you choose. Typically for an ATS system the process goes like this:
- Job posting is created via ATS
- The job posting is published on the company’s website or job posting sites.
- Candidates apply for the post
- ATS collects applications
- Candidates’ contact information, work experience, education information and other additional information are uploaded to the database.
- ATS screens candidates based on company-set filters
Automatic messages are sent to candidates as acceptance or rejection via ATS.
The ATS software scans all applications and resumes that do not meet the criteria set by the business. These criteria usually relate to technical skills, certifications and work experience. However, by integrating resume parser APIs into an ATS application that they will create themselves, they can have much more flexibility such as filtering, processing, and analyzing their resumes. With the resume parser API, which uses NLP algorithms, businesses can also provide consultancy services to different companies with their own ATS applications.
Candidate Experience Process
You may need to quickly review the thousands of resumes that come to your business. Although increasing the number of people in your team who analyze the resume to speed up this situation sounds like a solution, this method actually brings out the cost and unhappiness within the team.
When you consider the thousands of resume owners that need to be reviewed and are waiting for a return from your business, the fastest way to handle this process is to use the resume parser API. In this way, candidates who apply to your business will receive the return they expect from your business in a short time.
Finding the Most Suitable Candidate
In recruitment processes, recruiters often overlook the most suitable candidate when they have to review and analyze thousands of resumes. This is an overwhelming and difficult situation for recruiters. With the ATS programs you will develop with the Resume parser API, you can find the most suitable candidate without any emotion or misconception.
Integration Resume Parser API for Docx to Text Conversion
In this section, we will cover the code side integration of the resume parser API. We will see step by step how to integrate the Resume parser API into the Python programming language.
Before we integrate the Resume parser API into the code, we need to obtain an API key. From here, let’s go to the website of the resume parser API. Then let’s hit the ‘Subscribe for Free’ button.
Then the package options for registration will meet us.
Resume parser API offers a total of four package contents, including the free package. The packages it offers are both flexible and affordable packages. We will choose one of these packages and register and then we will get the API key.
After obtaining the API key, we can proceed to the integration of the resume parser API. We will create a python file on our computer that we will write our codes and then run. Let’s set the name of this file as ‘index.py’. Then paste the code below into the file we created.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
import requests url = "https://api.apilayer.com/resume_parser/url?url=https://assets.apilayer.com/apis/codes/resume_parser/sample_resume.docx" payload = {} headers= { "apikey": "I0*****xj" } response = requests.request("GET", url, headers=headers, data = payload) status_code = response.status_code result = response.json() print(result) |
We will review the code we pasted. First of all, when we want to look at the ‘url’ field, we see our resume parser API here. A resume url in docx format is sent as a parameter to this API. You can find the sample resume with docx format url here.
Then we pasted the API key we obtained after registering with the resume parser API in the ‘apikey’ in the ‘Headers’ field.
In the continuation of the code, we send the query we have prepared to the resume parser API and print the result on the console screen of the application. To run the application, let’s open a terminal from the file location of our ‘index.py’ file and run the following command.
|
1 |
python index.py |
Seconds after running the application, the following output is printed on the console of the application.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 |
{ "name": "JOHN DOE", "address": "09800", "skills": [ "Html5", "Adobe", "Computer science", "Javascript", "Illustrator", "Json", "Design", "Benchmark", "Programming", "Cms", "Wordpress", "C#", "C", "Sql", "Css", "Php", "Api", "Photoshop", "Html" ], "education": [ "dates": [ "June 2011", "August 2015" ], "date_start": "June 2011", "date_end": "August 2015", "location": "Plano", "organization": "VOKO COMMUNICATIONS" } ] } |
When we examine the output in JSON format printed on the console screen, we see that the resume parser API successfully classifies the information in the resume. Now let’s examine the parse elimination of the desired areas in the resume, which is the approach used especially in ATS applications.
- Extracting email from resumes
Email parsing from target resumes is frequently preferred by ATS applications. We will update the code that prints the output to the console screen, which is the last line in the application we have developed, as follows.
|
1 |
print(result["email"]) |
When we run the application again, we will see the following output on the console screen of the application.
|
1 |
JOHN DOE |
- Extracting skills from the resumes
Extracting the candidate’s skills from the resume is the most preferred way by ATS applications to find the right candidate. Businesses can quickly decide whether the candidate has the necessary skills by extracting skills from the resume via ATS applications. We will update the print method as follows.
|
1 |
print(result["skills"]) |
When we run the application, the following skills list will be printed on the console screen of the application.
|
1 |
['C#', 'Api', 'Html5', 'C', 'Cms', 'Php', 'Css', 'Computer science', 'Benchmark', 'Adobe', 'Design', 'Programming', 'Html', 'Javascript', 'Sql', 'Wordpress', 'Photoshop', 'Json', 'Illustrator'] |
- Extracting education and schools from resumes
Educational information is one of the most important criteria for recruiters. For this reason, recruiters extract training information from resumes. While performing the python extract data from word document operation with the Resume parser API, we can obtain the education information in the resume as follows.
|
1 |
print(result["education"]) |
After running the application again, detailed education information will be printed on the console screen as a JSON array.
|
1 2 3 4 5 6 7 8 |
[ { 'name': 'RIVER BROok university', 'dates': [ 'May 2011' ] } ] |
Conclusion
With the development of technology, APIs that provide resume parsing service have emerged, mostly to minimize errors in recruitment processes and to be used in artificial intelligence applications. APIs that provide Resume parsing service are especially used in ATS applications belonging to enterprises. If you want to find the most suitable candidate for your business among thousands or even hundreds of thousands of resumes in just seconds, explore the resume parser API published under Apilayer.
