What is named entity recognition (NER) in NLP?
Welcome to Answerclub.org
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Named entity recognition is the process of identifying and classifying named entities (such as persons, organizations, locations, etc.) in text documents.
Named Entity Recognition (NER) in natural language processing (NLP) is a task that involves identifying and categorizing named entities within a text into predefined categories such as person names, organizations, locations, dates, numerical expressions, and more.
The goal of NER is to extract and classify specific entities mentioned in the text, providing context and structure to unstructured text data. NER systems typically use machine learning algorithms, such as Conditional Random Fields (CRFs), Hidden Markov Models (HMMs), or deep learning architectures like Bidirectional LSTMs or Transformers, trained on labeled datasets.
NER is a crucial component in various NLP applications, including information extraction, question answering, document summarization, sentiment analysis, and more. By accurately identifying and categorizing named entities, NER systems enable better understanding and analysis of text data, facilitating tasks such as semantic search, content recommendation, and knowledge extraction.