Glossary

Named entity recognition (NER)

What is Named entity recognition (NER)?

Named entity recognition (NER) is a natural language processing technique that finds names of people, companies and places, as well as dates or amounts, in text and assigns each to a category. It turns free text into structured data. Example: the sender’s company, contact person, delivery date and invoice amount are extracted automatically from an email and transferred to a CRM.

How KI.ordo helps

The KI.ordo template ‘Extract names, companies, places and dates’ returns this information from a text as a structured list (JSON).

Learn more →

Matching templates

Extract names, companies, places and datesExtracts people, companies, places, dates and amounts from a text and outputs them as JSON, e.g. to pass on to other systems.View template →

Topics

NLP
Information extraction
Text analysis

Try KI.ordo

Agents, flows and templates in one workspace – free 24-hour demo.

Start demo

← All terms

Related terms

Extractive AI

Extractive AI refers to methods that pull existing information out of texts or documents rather than writing new content. The output comes verbatim or in a fixed structure from the source – such as invoice numbers from PDFs, key sentences for a summary or the relevant passage in answer to a question. Because nothing is invented, the risk of false statements is lower than with purely generative approaches; the two are often combined.

Information extraction
Documents
+1

Template

Natural Language Processing (NLP)

Natural language processing is the branch of AI that enables computers to analyse, understand and generate human language in text or speech. It covers tasks such as translation, classification, summarisation, sentiment analysis and question answering. Rules and statistical methods once dominated; today the field is shaped by large language models.

NLP
Language
+1

Coreference Resolution

Coreference resolution is a natural language processing task that identifies which expressions in a text refer to the same person, thing or matter. In “Ms Weber called. She would like a call back”, the system must understand that “she” means Ms Weber. This matters for summaries, information extraction and chatbots that need to keep track of references across several sentences or conversation turns.

NLP
Text understanding
+1

spaCy

spaCy is an open-source Python library for natural language processing, developed by the Berlin-based company Explosion. It splits texts into words and sentences, determines parts of speech and base forms, analyses sentence structure and recognises named entities such as people, companies or places. Pre-trained models are available for many languages, including German. spaCy is designed for speed and for use in real applications, for example to pre-process large volumes of documents.

NLP
Python
+1