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* document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
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1.7 KiB
A named entity is a "real-world object" that's assigned a name – for example, a person, a country, a product or a book title. spaCy can recognize various types of named entities in a document, by asking the model for a prediction. Because models are statistical and strongly depend on the examples they were trained on, this doesn't always work perfectly and might need some tuning later, depending on your use case.
Named entities are available as the ents property of a Doc:
### {executable="true"}
import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp(u"Apple is looking at buying U.K. startup for $1 billion")
for ent in doc.ents:
print(ent.text, ent.start_char, ent.end_char, ent.label_)
- Text: The original entity text.
- Start: Index of start of entity in the
Doc.- End: Index of end of entity in the
Doc.- Label: Entity label, i.e. type.
| Text | Start | End | Label | Description |
|---|---|---|---|---|
| Apple | 0 | 5 | ORG |
Companies, agencies, institutions. |
| U.K. | 27 | 31 | GPE |
Geopolitical entity, i.e. countries, cities, states. |
| $1 billion | 44 | 54 | MONEY |
Monetary values, including unit. |
Using spaCy's built-in displaCy visualizer, here's what our example sentence and its named entities look like:
import DisplaCyEntHtml from 'images/displacy-ent1.html'; import { Iframe } from 'components/embed'