NER with OpenAI and LangChain

Did you know that you can apply NER using LangChain? See how →

Named Entity Recognition (NER) is a natural language processing (NLP) technique used to identify and classify named entities within a text into predefined categories such as the names of persons, organizations, locations, dates, quantities, monetary values, percentages, and more. The primary goal of NER is to extract and categorize specific entities mentioned in unstructured text data to better understand the underlying information and relationships within the text.

NER involves several steps:

1. Tokenization: Breaking down the text into individual words or tokens.
2. Part-of-Speech Tagging: Assigning grammatical parts of speech (e.g., noun, verb, adjective) to each token.
3. Named Entity Classification: Identifying tokens that represent named entities and assigning them to predefined categories like person names, organization names, locations, etc.
4. Entity Extraction: Extracting the identified named entities along with their respective categories from the text.

NER systems can be rule-based, statistical, or machine learning-based. Machine learning-based approaches, particularly those using deep learning models like recurrent neural networks (RNNs)…