How to extract keywords from Workable Job Applications using generative AI
How to Extract Keywords from Workable Job Applications using Generative AI
If you're a human resources professional, you know how time-consuming and overwhelming it can be to sift through job applications to identify the best candidates. That's where generative AI can help. In this post, we'll show you how to use generative AI to automatically extract keywords from job applications in Workable, which can save you time and effort in the hiring process.
What is Keyword Extraction?
Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. In the case of job applications, it can help you quickly identify the skills, experience, and qualifications that are most relevant to the position you're hiring for.
Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases, and can be trained on a labeled dataset of text.
You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes.
Example Use Cases
Use cases for extracting keywords from job applications in Workable include:
- Categorizing and prioritizing job applications
- Identifying candidates with the right skills and experience
- Improving the hiring process
- Reducing bias in the hiring process
Teams that might find these use cases helpful include: human resources, recruiting, talent acquisition, and hiring managers.
Accessing and Analyzing Workable Job Applications
To get started, you'll need to have access to the job applications in Workable. Once you've accessed the applications, you can export them in CSV format. This will create a spreadsheet that you can analyze using generative AI tools.
One of the most commonly used generative AI tools for keyword extraction is Python's Natural Language Toolkit (NLTK). You can use NLTK to preprocess the text data, identify keywords, and measure their frequency. Alternatively, there are many SaaS tools available that offer keyword extraction services, such as IBM Watson, Aylien, and AirOps.
Once you've identified the keywords, you can use them to categorize and prioritize job applications, identify candidates with the right skills and experience, and improve the hiring process overall. This can help you find the best candidates more quickly and efficiently, and reduce bias in the hiring process.