Resume Classification: A Case Study

Challenges Professional recruiters and HR personnel manually review hundreds of resumes.Manual sorting of resumes into categories is inaccurate and time-consuming.No resume summaries are available. Xpertnest’s Solutions A Named Entity Recognizer (NER) was trained to detect key words and key phrases from the resume.Data was annotated for training.The NER used the presence or absence of desired keywords for sorting.Resume summaries were created based on key words and phrases found in the resumes. Value Delivered NER reduced the risk of human error.Summaries enabled HR personnel to review resumes quickly and decide whether candidates were suitable.The client saved time and was able to raise the quality of...

For a Legal Firm: Contract Review Using NLP

Challenges The client, a legal company, wanted to be able to search documents for lease review and due diligence.The system needed to be able to identify the key parameters defined by the legal expert and provide key insights.Automated summaries had to be prepared using parameters such as contract terms, party details, and compensation and liability clauses. Xpertnest’s Solutions Xpertnest used Natural Language Processing (NLP) and Artificial Intelligence (AI) to train a model on the key parameters to be identified in the documentData was annotated for module training.A solution was implemented to generate summaries of each contract. Value Delivered Accurate summaries of contract information.Minimal human...

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