Have you ever wondered how the data collected in unstructured form is processed, and what’s the actual benefit for the end user? Is it possible to relate labelled and unstructured data for natural language processing to produce results that help maximize the inference, and save time and effort? Can we minimize the manual effort and make it automated? The answer is Yes! One of the biggest challenges in the field of data science is to merge structured and unstructured text to generate recommendations to help reduce the effort in finding the best job candidate. Our solution, using Watson Studio and Watson Natural Language Understanding, caters to different data formats and data sources to help enable informed decision. Our motto is to select the right candidate to help in risk mitigation, enhance ROI, and increase credibility for the recruitment process. In this pattern, we’ll also demonstrate how to optimize the search query by reviewing generated recommendations which can provide more opportunities in evaluating different options for short listing candidates.
This code pattern will help developers who want to learn a new method for scanning text across different document formats and establish a relation with the data stored in the structured format in a database.