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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"} ## 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 entities) to unique identifiers, grounding the named entities into the "real
world". This requires 3 main components: 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} ### 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. [`Vocab`](/api/vocab) instance.
| Name | Type | Description | | 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 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 entities) to unique identifiers, grounding the named entities into the "real
world". It requires a Knowledge base, a function to generate plausible world". It requires a `KnowledgeBase`, as well as a function to generate
candidates from that Knowledge base given a certain textual mention, and a ML plausible candidates from that `KnowledgeBase` given a certain textual mention,
model to pick the right candidate, given the local context of the mention. and a ML model to pick the right candidate, given the local context of the
mention.
## Config and implementation {#config} ## 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 > #### ✏️ Things to try
> >
> 1. Add the components `"ner"` and `"sentencizer"` _before_ the entity linker. > 1. Add the components `"ner"` and `"sentencizer"` _before_ the
> The analysis should now show no problems, because requirements are met. > `"entity_linker"`. The analysis should now show no problems, because
> requirements are met.
```python ```python
### {executable="true"} ### {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". | | **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. | | **Sentence Boundary Detection** (SBD) | Finding and segmenting individual sentences. |
| **Named Entity Recognition** (NER) | Labelling named "real-world" objects, like persons, companies or locations. | | **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. | | **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. | | **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. | | **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 will give you the object and its encoded annotations, plus the "key" to decode
it. it.
## Knowledge Base {#kb} ## Knowledge base {#kb}
To support the entity linking task, spaCy stores external knowledge in a To support the entity linking task, spaCy stores external knowledge in a
[`KnowledgeBase`](/api/kb). The knowledge base (KB) uses the `Vocab` to store [`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 ### 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 candidates or entity identifiers. The [`EntityLinker`](/api/entitylinker) will
take this list of candidates as input, and disambiguate the mention to the most take this list of candidates as input, and disambiguate the mention to the most
probable identifier, given the document context. probable identifier, given the document context.