100 Best Named-Entity Recognition Videos


Named-entity recognition (NER) is a natural language processing (NLP) task that involves identifying and labeling named entities in text, such as people, organizations, locations, and dates. This can be useful for tasks such as information extraction and knowledge base construction, as it allows the system to identify and extract specific pieces of information from the text.

There are several different approaches to NER, including rule-based systems, which apply predefined rules to identify named entities, and machine learning-based systems, which learn to recognize named entities from a training dataset. NER systems typically use a combination of techniques, such as dictionary matching, regular expressions, and machine learning algorithms, to identify named entities in the text.

NER is used in a variety of applications, including information extraction, text summarization, and question answering. It is also used to improve the accuracy and effectiveness of other NLP tasks, such as machine translation and text classification.

See also:

100 Best GitHub: Named-Entity Recognition | Named-Entity Recognition & Dialog Systems 2017

[51x Feb 2018]