What is the proportion of CVs, from the batch of different CVs, in which the education or experience section was parsed accurately?

The parsing of education and experience data relies on a range of factors, including the format, language, document type, and the presence of tables or columns. 
 
Our parsing solution utilizes NLP and deep learning techniques, which means its effectiveness is contingent upon the specific resume dataset being processed. The performance and accuracy of the parsing solution are directly influenced by the characteristics of the data being analyzed.

We always try to achieve all entity extraction in the best possible way, but it all depends on various factors.
Additionally, we are always willing to work on and make improvements in any area where our clients may face any kind of challenges.
 
Furthermore,  if you encounter any difficulties or challenges in the parsing of resumes, we encourage you to share your concerns with us. By doing so, we can conduct an analysis and provide you with the necessary resolution. 
 
Your feedback is valuable to us, as it enables us to address any issues promptly and ensure a smoother parsing experience for you.

If you have any questions or concerns, please don't hesitate to reach out to us at support@rchilli.com