Research Topic · Peer-Reviewed

Named Entity Recognition

Named Entity Recognition (NER) is an important Artificial Intelligence (AI) technology used in Natural Language Processing (NLP) applications. NER uses machine learning algorithms to detect and classify important information in a text, such as the names of people, organizations, locations, dates, and other relevant …

Curated from this journal's research 📚 1 peer-reviewed article cited Cited 15× across the literature 🔖 ISSN 2689-4602 🗓 Reviewed June 2026

Overview

Named Entity Recognition (NER) is an important Artificial Intelligence (AI) technology used in Natural Language Processing (NLP) applications. NER uses machine learning algorithms to detect and classify important information in a text, such as the names of people, organizations, locations, dates, and other relevant entities. By recognizing these entities, NER enables AI systems to better understand natural language and extract structured information from unstructured text. It can be used in a variety of tasks, such as automated document summarization, question answering, information extraction, and text mining. NER can improve the accuracy and performance of AI-based applications, and is widely seen as an essential component for next-generation natural language processing systems.

Research published in this journal

1 peer-reviewed article, ranked by relevance. Each links to its DOI.

How this research is being cited

The 1 article above has been cited 15 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Named Entity Recognition, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Evolutionary Science (ISSN 2689-4602).

Journal editorial board
Maria Luisa Chiusano · Italy Adina-Elena Segneanu · Romania George Mikhailovsky · United States

This page summarises published research for orientation; it is not medical or professional advice.