Process / pipeline
Named Entity Recognition (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.
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Sources
- Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗
- Lample, G. et al. (2016). Neural Architectures for Named Entity Recognition. NAACL. DOI: 10.18653/v1/N16-1030 ↗
Related methods
Referenced by
Abbreviation ExpansionArgument MiningAspect-Based Sentiment AnalysisChunkingClinical Text MiningConstituency ParsingCoreference ResolutionCross-Document Entity TrackingDependency ParsingDomain-adaptive Named Entity RecognitionEntity LinkingEvent DetectionExplainable Named Entity RecognitionFrame AnalysisGender Bias DetectionHallucination DetectionInformation ExtractionKnowledge Graph ConstructionLexical SubstitutionMultimodal Named Entity RecognitionNegation DetectionOpen Information ExtractionQuestion AnsweringRelation ExtractionScientific Text MiningSelf-supervised named entity recognitionSemantic ParsingSemantic Role LabelingSlot FillingSpeculation DetectionStructured Text ExtractionTemporal Expression ExtractionText InfillingText NormalizationTimeline ExtractionWord Sense Disambiguation