NER
NER is an NLP technique that’s designed to detect and categorize named entities within text, including but not limited to person’s names, organization’s names, locations, and more. NER’s primary objective is to autonomously identify and extract information about these named entities from unstructured text data.
NER typically involves using machine learning models, such as conditional random fields (CRFs) or recurrent neural networks (RNNs), to tag words in a given sentence with their corresponding entity types. The models are trained on large annotated datasets that contain text with labeled entities. These models then use context-based rules to identify named entities in new text.
There are several categories of named entities that can be identified by NER, including the following:
- Person: A named individual, such as “Barack Obama”
- Organization: A named company, institution, or organization, such as “Google”...
