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Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names (PER), organizations (ORG), locations (LOC), geopolitical entities…
History, Problem & Approaches
Explore the main themes, entities and connections around Named-entity recognition. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
ner entity entities names systems example name text types named also many refer statistical recognition one person precision organization well
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| person names | instance of | is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories | 0.80 | text |
| CoNLL | instance of | In academic conferences | 0.80 | text |
| a variant of the F1 score has been defined as follows | instance of | In academic conferences | 0.80 | text |
| machine learning | instance of | ApproachesNER systems have been created that use linguistic grammar-based techniques as well as statistical models | 0.80 | text |
| HMM | instance of | Another challenging task is devising models to deal with linguistically complex contexts such as Twitter and search queries.There are some researchers who did some comparisons a… | 0.80 | text |
| Named-entity recognition | related to Approaches | NER | 0.60 | section |
| Named-entity recognition | related to Approaches | State | 0.60 | section |
| Named-entity recognition | related to Approaches | GATE | 0.60 | section |
| Named-entity recognition | related to Approaches | Java API | 0.60 | section |
| Named-entity recognition | related to Approaches | OpenNLP | 0.60 | section |
| Named-entity recognition | related to Current challenges | Despite | 0.60 | section |
| Named-entity recognition | related to Current challenges | F1 | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.