If you're like most HR professionals, you know the pain of resume parsing all too well. You've probably spent hours manually cataloging job candidates' information, only to have your system spit out inaccurate data. But what if I told you that there's a solution to this puzzle? Deep learning can help you automate the resume parsing process, saving you time and ensuring accuracy. In this blog post, I'll show you how it works. Stay tuned!
How Does One Create A Resume Parser?
Creating a resume parser requires understanding the fundamentals of deep learning. Deep learning is a type of artificial intelligence (AI) that uses algorithms to identify patterns in data sets and use them to make decisions or predictions. By using this technology, you can teach your resume parser how to read resumes and extract information such as the candidate's name, previous job titles, and work experience.
Using a resume parser has many advantages for HR professionals. For starters, it streamlines the recruitment process by automatically sorting through applications and shortlisting candidates based on their qualifications. This makes it easier to quickly find the right fit for open roles. Additionally, automated resume parsing helps ensure accuracy in candidate data, since it eliminates the potential for human error. It also reduces time spent on mundane tasks like manually entering information into your systems, freeing up HR professionals to focus on what matters: finding and hiring the best talent.
How Does a Resume Parser Work?
A resume parser typically consists of two components: an artificial neural network and a natural language processing algorithm. The neural network is responsible for interpreting the incoming data from resumes, while the NLP algorithm is used to extract relevant information from the documents. This process starts with parsing the resume's text into smaller chunks, such as individual sentences or words. Then, the parser uses its knowledge of language to identify key phrases and concepts in each sentence, including job titles and work experience. Finally, it stores this information in a structured format so that it can be accessed by other systems.
By leveraging deep learning technology, you can create a powerful resume parser that automates the recruitment process and ensures accuracy in candidate data. With automated resume parsing at your fingertips, you can save time and get the most out of your recruitment efforts.
Extraction of text
Text extraction is an important part of the resume parsing process. Once the parser has identified key phrases and concepts, it needs to extract the relevant text to store it in a structured format. To do this, it will use its understanding of language to recognize patterns in text that indicate where a block of information begins and ends. For example, if it sees words like “job title” or “work experience” together in one sentence, it can assume that the information following those words is relevant. Extraction helps ensure accuracy by eliminating any additional irrelevant data from being stored.
The Problems of Resume Parsers
Resume parsers face a unique challenge due to the number of different resume formats out there. Even though two resumes may look similar on the surface, they can contain completely different text formatting that makes them difficult to read for computer algorithms. Tables, columns, and graphics can all be used to present information, making it harder for rule-based parsers alone to extract relevant data from raw documents in formats like PDFs and DOCs. To overcome this issue, an intelligent algorithm is needed to accurately parse and extract text from these documents in a meaningful way.
Methods for Creating a Resume Parser
Creating a successful resume parser requires combining different methods to achieve the best results. Several approaches can be used, such as rule-based parsers and natural language processing algorithms. Rule-based parsers look for specific patterns in text that indicate where a block of information begins or ends. Meanwhile, natural language processing algorithms use their understanding of language to identify key phrases and concepts from resumes. Combining both techniques can provide an effective way to accurately parse raw documents into structured data.
We created a new classification system to divide resumes into distinct types and address each one accordingly. Many of the templates, such as those that contain tables or partitions, needed more advanced software to read them accurately. To achieve this, we used a combination of Optical Character Recognition (OCR) and Deep Natural Language Processing (NLP) algorithms to extract the relevant text from these documents.
Rather than taking the difficult route, we chose to go with a smarter solution. Optical Character Recognition (OCR) and Deep Natural Language Processing (NLP) have been extensively studied by leading technology companies and their findings are shared through open-source software. This allowed us to use existing models rather than building a deep learning model from scratch, saving time and effort while still achieving accurate results.
Introducing Hirize Resume Parser, a deep learning-based resume parsing software. This powerful tool automates the recruitment process and ensures accuracy in candidate data with its combination of rule-based parsing and natural language processing (NLP). Thanks to OCR and NLP algorithms, it accurately parses raw documents into structured data at lightning speed without sacrificing accuracy. No more manual cataloging of job candidates' information. Try Hirze today!
