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svlandeg 2020-08-06 23:20:13 +02:00
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website/docs/api/architectures.md
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@ -260,7 +260,7 @@ If the `nO` dimension is not set, the TextCategorizer component will set it when
## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}
An Entity Linker component disambiguates textual mentions (tagged as named
An `EntityLinker` component disambiguates textual mentions (tagged as named
entities) to unique identifiers, grounding the named entities into the "real
world". This requires 3 main components:
@ -312,7 +312,7 @@ If the `nO` dimension is not set, the Entity Linking component will set it when
### spacy.EmptyKB.v1 {#EmptyKB}
A function that creates a default, empty Knowledge Base from a
A function that creates a default, empty `KnowledgeBase` from a
[`Vocab`](/api/vocab) instance.
| Name | Type | Description |

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website/docs/api/entitylinker.md
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@ -9,11 +9,12 @@ api_string_name: entity_linker
api_trainable: true
---
An Entity Linker component disambiguates textual mentions (tagged as named
An `EntityLinker` component disambiguates textual mentions (tagged as named
entities) to unique identifiers, grounding the named entities into the "real
world". It requires a Knowledge base, a function to generate plausible
candidates from that Knowledge base given a certain textual mention, and a ML
model to pick the right candidate, given the local context of the mention.
world". It requires a `KnowledgeBase`, as well as a function to generate
plausible candidates from that `KnowledgeBase` given a certain textual mention,
and a ML model to pick the right candidate, given the local context of the
mention.
## Config and implementation {#config}

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website/docs/usage/processing-pipelines.md
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@ -380,8 +380,9 @@ table instead of only returning the structured data.
> #### ✏️ Things to try
>
> 1. Add the components `"ner"` and `"sentencizer"` _before_ the entity linker.
> The analysis should now show no problems, because requirements are met.
> 1. Add the components `"ner"` and `"sentencizer"` _before_ the
> `"entity_linker"`. The analysis should now show no problems, because
> requirements are met.
```python
### {executable="true"}

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website/docs/usage/spacy-101.md
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@ -122,7 +122,7 @@ related to more general machine learning functionality.
| **Lemmatization** | Assigning the base forms of words. For example, the lemma of "was" is "be", and the lemma of "rats" is "rat". |
| **Sentence Boundary Detection** (SBD) | Finding and segmenting individual sentences. |
| **Named Entity Recognition** (NER) | Labelling named "real-world" objects, like persons, companies or locations. |
| **Entity Linking** (EL) | Disambiguating textual entities to unique identifiers in a Knowledge Base. |
| **Entity Linking** (EL) | Disambiguating textual entities to unique identifiers in a knowledge base. |
| **Similarity** | Comparing words, text spans and documents and how similar they are to each other. |
| **Text Classification** | Assigning categories or labels to a whole document, or parts of a document. |
| **Rule-based Matching** | Finding sequences of tokens based on their texts and linguistic annotations, similar to regular expressions. |
@ -379,7 +379,7 @@ spaCy will also export the `Vocab` when you save a `Doc` or `nlp` object. This
will give you the object and its encoded annotations, plus the "key" to decode
it.
## Knowledge Base {#kb}
## Knowledge base {#kb}
To support the entity linking task, spaCy stores external knowledge in a
[`KnowledgeBase`](/api/kb). The knowledge base (KB) uses the `Vocab` to store
@ -426,7 +426,7 @@ print("Number of aliases in KB:", kb.get_size_aliases()) # 2
### Candidate generation
Given a textual entity, the Knowledge Base can provide a list of plausible
Given a textual entity, the knowledge base can provide a list of plausible
candidates or entity identifiers. The [`EntityLinker`](/api/entitylinker) will
take this list of candidates as input, and disambiguate the mention to the most
probable identifier, given the document context.