Entity-Recognition-In-Resumes-SpaCy | Automatic Summarization of Resumes with NER - > Evaluate | Natural Language Processing library

Entity-Recognition-In-Resumes-SpaCy is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. Entity-Recognition-In-Resumes-SpaCy has no bugs, it has no vulnerabilities and it has low support. However Entity-Recognition-In-Resumes-SpaCy build file is not available. You can download it from GitHub.

Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a sub-task of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. NER systems have been created that use linguistic grammar-based techniques as well as statistical models such as machine learning. Hand-crafted grammar-based systems typically obtain better precision, but at the cost of lower recall and months of work by experienced computational linguists . Statistical NER systems typically require a large amount of manually annotated training data. Semisupervised approaches have been suggested to avoid part of the annotation effort.