mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-10 19:57:17 +03:00
0ba1b5eebc
* 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 * turn kb_creator into CLI script (wip) * proper parameters for training entity vectors * wikidata pipeline split up into two executable scripts * remove context_width * move wikidata scripts in bin directory, remove old dummy script * refine KB script with logs and preprocessing options * small edits * small improvements to logging of EL CLI script
142 lines
6.0 KiB
Python
142 lines
6.0 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import bz2
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import json
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import datetime
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def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
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# Read the JSON wiki data and parse out the entities. Takes about 7u30 to parse 55M lines.
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# get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
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lang = "en"
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site_filter = "enwiki"
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# properties filter (currently disabled to get ALL data)
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prop_filter = dict()
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# prop_filter = {'P31': {'Q5', 'Q15632617'}} # currently defined as OR: one property suffices to be selected
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title_to_id = dict()
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id_to_descr = dict()
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# parse appropriate fields - depending on what we need in the KB
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parse_properties = False
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parse_sitelinks = True
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parse_labels = False
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parse_descriptions = True
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parse_aliases = False
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parse_claims = False
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with bz2.open(wikidata_file, mode="rb") as file:
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line = file.readline()
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cnt = 0
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while line and (not limit or cnt < limit):
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if cnt % 1000000 == 0:
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print(
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datetime.datetime.now(), "processed", cnt, "lines of WikiData JSON dump"
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)
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clean_line = line.strip()
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if clean_line.endswith(b","):
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clean_line = clean_line[:-1]
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if len(clean_line) > 1:
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obj = json.loads(clean_line)
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entry_type = obj["type"]
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if entry_type == "item":
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# filtering records on their properties (currently disabled to get ALL data)
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# keep = False
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keep = True
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claims = obj["claims"]
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if parse_claims:
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for prop, value_set in prop_filter.items():
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claim_property = claims.get(prop, None)
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if claim_property:
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for cp in claim_property:
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cp_id = (
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cp["mainsnak"]
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.get("datavalue", {})
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.get("value", {})
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.get("id")
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)
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cp_rank = cp["rank"]
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if cp_rank != "deprecated" and cp_id in value_set:
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keep = True
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if keep:
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unique_id = obj["id"]
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if to_print:
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print("ID:", unique_id)
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print("type:", entry_type)
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# parsing all properties that refer to other entities
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if parse_properties:
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for prop, claim_property in claims.items():
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cp_dicts = [
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cp["mainsnak"]["datavalue"].get("value")
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for cp in claim_property
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if cp["mainsnak"].get("datavalue")
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]
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cp_values = [
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cp_dict.get("id")
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for cp_dict in cp_dicts
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if isinstance(cp_dict, dict)
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if cp_dict.get("id") is not None
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]
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if cp_values:
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if to_print:
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print("prop:", prop, cp_values)
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found_link = False
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if parse_sitelinks:
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site_value = obj["sitelinks"].get(site_filter, None)
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if site_value:
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site = site_value["title"]
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if to_print:
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print(site_filter, ":", site)
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title_to_id[site] = unique_id
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found_link = True
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if parse_labels:
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labels = obj["labels"]
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if labels:
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lang_label = labels.get(lang, None)
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if lang_label:
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if to_print:
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print(
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"label (" + lang + "):", lang_label["value"]
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)
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if found_link and parse_descriptions:
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descriptions = obj["descriptions"]
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if descriptions:
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lang_descr = descriptions.get(lang, None)
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if lang_descr:
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if to_print:
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print(
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"description (" + lang + "):",
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lang_descr["value"],
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)
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id_to_descr[unique_id] = lang_descr["value"]
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if parse_aliases:
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aliases = obj["aliases"]
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if aliases:
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lang_aliases = aliases.get(lang, None)
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if lang_aliases:
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for item in lang_aliases:
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if to_print:
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print(
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"alias (" + lang + "):", item["value"]
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)
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if to_print:
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print()
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line = file.readline()
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cnt += 1
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print(datetime.datetime.now(), "processed", cnt, "lines of WikiData JSON dump")
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return title_to_id, id_to_descr
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