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I am working on a resume parser project. Currently, I am using rule-based regex to extract features like University, Experience, Large Companies, etc.

So basically I have a set of universities' names in a CSV, and if the resume contains one of them then I am extracting that as University Name. In the same way I have a list of Large Companies in CSV and if the resume contains any of them then I flag it as Yes.

So these are rule-based logic and can never be fool-proof considering different countries have different resume formats. Is there any other way of doing it to improve the accuracy and make it a global solution?

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Yes there is. Look at this paper of general information extraction

Where you can construct features and generalise around extracting, without any hard coded rules.

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  • $\begingroup$ Thank you.. I will go through the document. Any sample code available for the same ? $\endgroup$ – Akash Jan 17 at 9:52
  • $\begingroup$ No they did not, which I find bad but I guess its their product possibility $\endgroup$ – Noah Weber Jan 17 at 9:57

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