Named Entity Recognition (NER)
Also known as: NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.
Key highlights
- Turns unstructured text into structured, typed entity spans that downstream tasks can consume.
- Spans multiple approaches: rule-based matching for small problems, neural sequence labelling for large ones.
- Identifies several entity classes — people, organisations, locations, dates — in a single pass over the text.
Intuition
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How it works
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When to use it
NER fits when you have text data and a language model that supports the language of that text, with preprocessing applied first. A reasonable amount of text is needed; with a very small corpus the model performs poorly and simple rule-based keyword extraction is the safer choice. If there is no text data at all, NER cannot run and you should analyse structured content instead.
Strengths & limitations
- Turns unstructured text into structured, typed entity spans that downstream tasks can consume.
- Spans multiple approaches: rule-based matching for small problems, neural sequence labelling for large ones.
- Identifies several entity classes — people, organisations, locations, dates — in a single pass over the text.
- On a small corpus model performance drops, and simple rule-based keyword extraction becomes the better option.
- Performance depends on the language model supporting the target language of the text.
- Requires preprocessing; without clean tokenisation entity boundaries are detected unreliably.
Common pitfalls
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Applications
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Frequently asked
Rule-based, machine learning, or neural — which approach should I use?
A rule-based approach matches patterns and dictionaries and works on small problems with no labelled data. A machine-learning sequence labeller learns from labelled examples and adapts to your domain. A neural approach uses contextual architectures and generally performs best on large corpora, at higher computational cost.
What happens if my corpus is very small?
On a small corpus (roughly under 100 documents) the NER model's performance drops. In that case a simpler rule-based keyword extraction approach is preferred over a full entity-recognition model.
Does NER work in languages other than English?
Yes, but the chosen language model must support the target language of the text. A model that does not cover the corpus language is a common cause of poor recognition.
How is NER evaluated?
Tagged spans are compared against labelled data using precision, recall, and F1. Inspecting missed and incorrect entities matters, because a tagger can quietly fail to detect an entire entity class while still looking accurate overall.
Sources
- 1.Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
- 2.Lample, G. et al. (2016). Neural Architectures for Named Entity Recognition. NAACL.
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Cite this page
ScholarGate. (2026, June 1). Named Entity Recognition. ScholarGate. https://scholargate.app/text-mining/named-entity-recognition