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			110 lines
		
	
	
		
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			110 lines
		
	
	
		
			2.7 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
| //- 💫 DOCS > API > ANNOTATION > NAMED ENTITIES
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| 
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| p
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|     |  Models trained on the
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|     |  #[+a("https://catalog.ldc.upenn.edu/ldc2013t19") OntoNotes 5] corpus
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|     |  support the following entity types:
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| 
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| +table(["Type", "Description"])
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|     +row
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|         +cell #[code PERSON]
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|         +cell People, including fictional.
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| 
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|     +row
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|         +cell #[code NORP]
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|         +cell Nationalities or religious or political groups.
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| 
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|     +row
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|         +cell #[code FACILITY]
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|         +cell Buildings, airports, highways, bridges, etc.
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| 
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|     +row
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|         +cell #[code ORG]
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|         +cell Companies, agencies, institutions, etc.
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| 
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|     +row
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|         +cell #[code GPE]
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|         +cell Countries, cities, states.
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| 
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|     +row
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|         +cell #[code LOC]
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|         +cell Non-GPE locations, mountain ranges, bodies of water.
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| 
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|     +row
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|         +cell #[code PRODUCT]
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|         +cell Objects, vehicles, foods, etc. (Not services.)
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| 
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|     +row
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|         +cell #[code EVENT]
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|         +cell Named hurricanes, battles, wars, sports events, etc.
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| 
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|     +row
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|         +cell #[code WORK_OF_ART]
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|         +cell Titles of books, songs, etc.
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| 
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|     +row
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|         +cell #[code LAW]
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|         +cell Named documents made into laws.
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| 
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|     +row
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|         +cell #[code LANGUAGE]
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|         +cell Any named language.
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| 
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|     +row
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|         +cell #[code DATE]
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|         +cell Absolute or relative dates or periods.
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| 
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|     +row
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|         +cell #[code TIME]
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|         +cell Times smaller than a day.
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| 
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|     +row
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|         +cell #[code PERCENT]
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|         +cell Percentage, including "%".
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| 
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|     +row
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|         +cell #[code MONEY]
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|         +cell Monetary values, including unit.
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| 
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|     +row
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|         +cell #[code QUANTITY]
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|         +cell Measurements, as of weight or distance.
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| 
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|     +row
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|         +cell #[code ORDINAL]
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|         +cell "first", "second", etc.
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| 
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|     +row
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|         +cell #[code CARDINAL]
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|         +cell Numerals that do not fall under another type.
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| 
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| +h(4, "ner-wikipedia-scheme") Wikipedia scheme
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| 
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| p
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|     |  Models trained on Wikipedia corpus
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|     |  (#[+a("http://www.sciencedirect.com/science/article/pii/S0004370212000276") Nothman et al., 2013])
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|     |  use a less fine-grained NER annotation scheme and recognise the
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|     |  following entities:
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| 
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| +table(["Type", "Description"])
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|     +row
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|         +cell #[code PER]
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|         +cell Named person or family.
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| 
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|     +row
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|         +cell #[code LOC]
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|         +cell
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|             |  Name of politically or geographically defined location (cities,
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|             |  provinces, countries, international regions, bodies of water,
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|             |  mountains).
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| 
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|     +row
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|         +cell #[code ORG]
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|         +cell Named corporate, governmental, or other organizational entity.
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| 
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|     +row
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|         +cell #[code MISC]
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|         +cell
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|             |  Miscellaneous entities, e.g. events, nationalities, products or
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|             |  works of art.
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