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Rename Candidate to InMemoryCandidate, BaseCandidate to Candidate.
This commit is contained in:
Raphael Mitsch
parent
417e8fea8b
commit
49abf4fb3a
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@ -1,5 +1,5 @@
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from .kb import KnowledgeBase
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from .kb_in_memory import InMemoryLookupKB
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from .candidate import Candidate
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from .candidate import Candidate, InMemoryCandidate
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__all__ = ["KnowledgeBase", "InMemoryLookupKB", "Candidate"]
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__all__ = ["KnowledgeBase", "InMemoryLookupKB", "Candidate", "InMemoryCandidate"]
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@ -2,8 +2,8 @@ import abc
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from typing import List, Union, Callable
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class BaseCandidate(abc.ABC):
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"""A `BaseCandidate` object refers to a textual mention (`alias`) that may or may not be resolved
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class Candidate(abc.ABC):
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"""A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
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to a specific `entity_id` from a Knowledge Base. This will be used as input for the entity_id linking
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algorithm which will disambiguate the various candidates to the correct one.
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Each candidate (alias, entity_id) pair is assigned a certain prior probability.
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@ -14,7 +14,7 @@ class BaseCandidate(abc.ABC):
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def __init__(
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self, mention: str, entity_id: Union[int, str], entity_vector: List[float]
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):
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"""Initializes properties of `BaseCandidate`.
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"""Initializes properties of `Candidate` instance.
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mention (str): Mention text for this candidate.
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entity_id (Union[int, str]): Unique entity ID.
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entity_vector (List[float]): Entity embedding.
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@ -44,8 +44,8 @@ class BaseCandidate(abc.ABC):
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return self._entity_vector
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class Candidate(BaseCandidate):
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"""`Candidate` for InMemoryLookupKB."""
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class InMemoryCandidate(Candidate):
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"""Candidate for InMemoryLookupKB."""
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def __init__(
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self,
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@ -36,7 +36,7 @@ cdef class KnowledgeBase:
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and the prior probability of that alias resolving to that entity.
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If no candidate is found for a given text, an empty list is returned.
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mentions (Iterable[Span]): Mentions for which to get candidates.
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RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
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RETURNS (Iterable[Iterable[InMemoryCandidate]]): Identified candidates.
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"""
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return [self.get_candidates(span) for span in mentions]
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@ -46,7 +46,7 @@ cdef class KnowledgeBase:
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and the prior probability of that alias resolving to that entity.
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If the no candidate is found for a given text, an empty list is returned.
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mention (Span): Mention for which to get candidates.
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RETURNS (Iterable[Candidate]): Identified candidates.
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RETURNS (Iterable[InMemoryCandidate]): Identified candidates.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
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@ -18,7 +18,7 @@ from .. import util
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from ..util import SimpleFrozenList, ensure_path
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from ..vocab cimport Vocab
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from .kb cimport KnowledgeBase
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from .candidate import Candidate as Candidate
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from .candidate import InMemoryCandidate
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cdef class InMemoryLookupKB(KnowledgeBase):
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@ -223,10 +223,10 @@ cdef class InMemoryLookupKB(KnowledgeBase):
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alias_entry.probs = probs
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self._aliases_table[alias_index] = alias_entry
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def get_candidates(self, mention: Span) -> Iterable[Candidate]:
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def get_candidates(self, mention: Span) -> Iterable[InMemoryCandidate]:
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return self.get_alias_candidates(mention.text) # type: ignore
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def get_alias_candidates(self, str alias) -> Iterable[Candidate]:
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def get_alias_candidates(self, str alias) -> Iterable[InMemoryCandidate]:
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"""
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Return candidate entities for an alias. Each candidate defines the entity, the original alias,
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and the prior probability of that alias resolving to that entity.
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@ -239,7 +239,7 @@ cdef class InMemoryLookupKB(KnowledgeBase):
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alias_entry = self._aliases_table[alias_index]
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return [
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Candidate(
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InMemoryCandidate(
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retrieve_string_from_hash=self.vocab.strings.__getitem__,
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entity_hash=self._entries[entry_index].entity_hash,
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entity_freq=self._entries[entry_index].freq,
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@ -116,7 +116,7 @@ def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
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Return candidate entities for a given mention and fetching appropriate entries from the index.
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kb (KnowledgeBase): Knowledge base to query.
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mention (Span): Entity mention for which to identify candidates.
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RETURNS (Iterable[Candidate]): Identified candidates.
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RETURNS (Iterable[InMemoryCandidate]): Identified candidates.
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"""
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return kb.get_candidates(mention)
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@ -128,6 +128,6 @@ def get_candidates_batch(
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Return candidate entities for the given mentions and fetching appropriate entries from the index.
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kb (KnowledgeBase): Knowledge base to query.
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mention (Iterable[Span]): Entity mentions for which to identify candidates.
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RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
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RETURNS (Iterable[Iterable[InMemoryCandidate]]): Identified candidates.
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"""
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return kb.get_candidates_batch(mentions)
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@ -7,7 +7,7 @@ from thinc.types import Ragged
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from spacy import registry, util
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from spacy.attrs import ENT_KB_ID
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from spacy.compat import pickle
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from spacy.kb import Candidate, InMemoryLookupKB, KnowledgeBase
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from spacy.kb import InMemoryCandidate, InMemoryLookupKB, KnowledgeBase
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from spacy.lang.en import English
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from spacy.ml import load_kb
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from spacy.ml.models.entity_linker import build_span_maker, get_candidates
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@ -506,13 +506,13 @@ def test_el_pipe_configuration(nlp):
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@registry.misc("spacy.LowercaseCandidateGenerator.v1")
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def create_candidates() -> Callable[
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[InMemoryLookupKB, "Span"], Iterable[Candidate]
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[InMemoryLookupKB, "Span"], Iterable[InMemoryCandidate]
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]:
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return get_lowercased_candidates
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@registry.misc("spacy.LowercaseCandidateBatchGenerator.v1")
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def create_candidates_batch() -> Callable[
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[InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]
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[InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[InMemoryCandidate]]
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]:
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return get_lowercased_candidates_batch
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@ -10,9 +10,9 @@ version: 3.5
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The `InMemoryLookupKB` class inherits from [`KnowledgeBase`](/api/kb) and
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implements all of its methods. It stores all KB data in-memory and generates
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[`Candidate`](/api/kb#candidate) objects by exactly matching mentions with
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entity names. It's highly optimized for both a low memory footprint and speed of
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retrieval.
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[`InMemoryCandidate`](/api/kb#candidate) objects by exactly matching mentions
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with entity names. It's highly optimized for both a low memory footprint and
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speed of retrieval.
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## InMemoryLookupKB.\_\_init\_\_ {id="init",tag="method"}
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@ -156,7 +156,7 @@ Get a list of all aliases in the knowledge base.
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## InMemoryLookupKB.get_candidates {id="get_candidates",tag="method"}
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Given a certain textual mention as input, retrieve a list of candidate entities
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of type [`Candidate`](/api/kb#candidate). Wraps
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of type [`InMemoryCandidate`](/api/kb#candidate). Wraps
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[`get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
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> #### Example
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@ -168,10 +168,10 @@ of type [`Candidate`](/api/kb#candidate). Wraps
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> candidates = kb.get_candidates(doc[0:2])
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> ```
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| Name | Description |
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| ----------- | -------------------------------------------------------------------- |
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| `mention` | The textual mention or alias. ~~Span~~ |
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| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------------ |
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| `mention` | The textual mention or alias. ~~Span~~ |
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| **RETURNS** | An iterable of relevant `InMemoryCandidate` objects. ~~Iterable[InMemoryCandidate]~~ |
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## InMemoryLookupKB.get_candidates_batch {id="get_candidates_batch",tag="method"}
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@ -194,15 +194,15 @@ to you.
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> candidates = kb.get_candidates((doc[0:2], doc[3:]))
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> ```
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| Name | Description |
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| ----------- | -------------------------------------------------------------------------------------------- |
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| `mentions` | The textual mention or alias. ~~Iterable[Span]~~ |
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| **RETURNS** | An iterable of iterable with relevant `Candidate` objects. ~~Iterable[Iterable[Candidate]]~~ |
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------------------------------------ |
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| `mentions` | The textual mention or alias. ~~Iterable[Span]~~ |
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| **RETURNS** | An iterable of iterable with relevant `InMemoryCandidate` objects. ~~Iterable[Iterable[InMemoryCandidate]]~~ |
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## InMemoryLookupKB.get_alias_candidates {id="get_alias_candidates",tag="method"}
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Given a certain textual mention as input, retrieve a list of candidate entities
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of type [`Candidate`](/api/kb#candidate).
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of type [`InMemoryCandidate`](/api/kb#candidate).
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> #### Example
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>
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@ -210,10 +210,10 @@ of type [`Candidate`](/api/kb#candidate).
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> candidates = kb.get_alias_candidates("Douglas")
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------- |
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| `alias` | The textual mention or alias. ~~str~~ |
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| **RETURNS** | The list of relevant `Candidate` objects. ~~List[Candidate]~~ |
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| Name | Description |
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| ----------- | ----------------------------------------------------------------------------- |
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| `alias` | The textual mention or alias. ~~str~~ |
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| **RETURNS** | The list of relevant `InMemoryCandidate` objects. ~~List[InMemoryCandidate]~~ |
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## InMemoryLookupKB.get_vector {id="get_vector",tag="method"}
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@ -9,8 +9,8 @@ version: 2.2
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---
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The `KnowledgeBase` object is an abstract class providing a method to generate
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[`Candidate`](/api/kb#candidate) objects, which are plausible external
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identifiers given a certain textual mention. Each such `Candidate` holds
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[`InMemoryCandidate`](/api/kb#candidate) objects, which are plausible external
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identifiers given a certain textual mention. Each such `InMemoryCandidate` holds
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information from the relevant KB entities, such as its frequency in text and
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possible aliases. Each entity in the knowledge base also has a pretrained entity
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vector of a fixed size.
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@ -72,10 +72,10 @@ of type [`Candidate`](/api/kb#candidate).
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> candidates = kb.get_candidates(doc[0:2])
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> ```
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| Name | Description |
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| ----------- | -------------------------------------------------------------------- |
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| `mention` | The textual mention or alias. ~~Span~~ |
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| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[Candidate]~~ |
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| Name | Description |
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| ----------- | ---------------------------------------------------------------------------- |
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| `mention` | The textual mention or alias. ~~Span~~ |
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| **RETURNS** | An iterable of relevant `Candidate` objects. ~~Iterable[InMemoryCandidate]~~ |
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## KnowledgeBase.get_candidates_batch {id="get_candidates_batch",tag="method"}
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@ -190,25 +190,27 @@ Restore the state of the knowledge base from a given directory. Note that the
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| `exclude` | List of components to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `KnowledgeBase` object. ~~KnowledgeBase~~ |
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## Candidate {id="candidate",tag="class"}
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## InMemoryCandidate {id="candidate",tag="class"}
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A `Candidate` object refers to a textual mention (alias) that may or may not be
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resolved to a specific entity from a `KnowledgeBase`. This will be used as input
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for the entity linking algorithm which will disambiguate the various candidates
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to the correct one. Each candidate `(alias, entity)` pair is assigned to a
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certain prior probability.
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A `InMemoryCandidate` object refers to a textual mention (alias) that may or may
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not be resolved to a specific entity from a `KnowledgeBase`. This will be used
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as input for the entity linking algorithm which will disambiguate the various
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candidates to the correct one. Each candidate `(alias, entity)` pair is assigned
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to a certain prior probability.
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### Candidate.\_\_init\_\_ {id="candidate-init",tag="method"}
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### InMemoryCandidate.\_\_init\_\_ {id="candidate-init",tag="method"}
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Construct a `Candidate` object. Usually this constructor is not called directly,
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but instead these objects are returned by the `get_candidates` method of the
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[`entity_linker`](/api/entitylinker) pipe.
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Construct a `InMemoryCandidate` object. Usually this constructor is not called
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directly, but instead these objects are returned by the `get_candidates` method
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of the [`entity_linker`](/api/entitylinker) pipe.
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> #### Example
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> #### Example```python
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>
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> from spacy.kb import InMemoryCandidate candidate = InMemoryCandidate(kb,
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> entity_hash, entity_freq, entity_vector, alias_hash, prior_prob)
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>
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> ```
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>
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> ```python
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> from spacy.kb import Candidate
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> candidate = Candidate(kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob)
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> ```
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| Name | Description |
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@ -219,7 +221,7 @@ but instead these objects are returned by the `get_candidates` method of the
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| `alias_hash` | The hash of the textual mention or alias. ~~int~~ |
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| `prior_prob` | The prior probability of the `alias` referring to the `entity`. ~~float~~ |
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## Candidate attributes {id="candidate-attributes"}
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## InMemoryCandidate attributes {id="candidate-attributes"}
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| Name | Description |
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| --------------- | ------------------------------------------------------------------------ |
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