Named Entity Recognition (NER)
A natural language processing technique that automatically finds and labels important names like people, places, or companies in a piece of text.
Named Entity Recognition (NER) is a natural language processing (NLP) technique that identifies and classifies key elements, such as names of people, organizations, locations, dates, and monetary values, within unstructured text. This process transforms raw text into structured data, facilitating tasks like information retrieval, content categorization, and data analysis.
For example, if a sentence says, “Apple opened a new office in London,” NER can spot “Apple” as a company and “London” as a location. This helps turn messy text into useful, structured data.
NER is helpful in many areas, such as search engines, news filtering, and customer service bots. It lets machines pick out key details from large amounts of text quickly and accurately. To do this well, NER systems often use machine learning, which means they learn from examples to become better at spotting the right words in the right context.
In localization, NER plays an important role by making sure that names, dates, or other special terms are handled correctly throughout translated content. It helps keep translations accurate, especially when the same word could mean different things in different languages or contexts.
There are different ways to build NER systems. Some follow fixed rules, while others are trained with data and can adapt over time. Both approaches aim to make it easier for computers to understand language the way humans do.
🔍 Core functions of NER: #️⃣
- Entity identification. Detects specific terms in-text, e.g., recognizing “Marie Curie” as a person or “Geneva” as a location.
- Classification. Categorizes identified entities into predefined groups like Person, Organization, or Location.
- Contextual understanding. Considers surrounding text to accurately interpret entities, reducing ambiguity.
