spikex
1.0.0
Spikex是一系列准备插入Spacy管道的管道。它旨在帮助以几乎零的精力来构建知识提取工具。
威克拉普从未如此快速闪电：
Doc的下划线扩展。示例是nounphrasex和verbphrasex ，分别提取名词短语和动词短语一些要求是从Spacy继承的：
一些依赖项使用Cython ，需要在Spikex之前安装它：
pip install cython请记住，始终建议使用虚拟环境，以避免修改系统状态。
此时，安装Spikex通过PIP是一行命令：
pip install spikexSpikex管与Spacy一起使用，因此需要安装它的模型。在这里遵循官方指示。支持全新的Spacy 3.0！
WikiGraph是由Wikipedia的一些关键组成部分开始构建的：页面，类别及其之间的关系。
根据维基百科垃圾场的大小，创建WikiGraph可能需要时间。因此，我们提供准备使用的Wikigraphs：
| 日期 | 威克拉普 | 朗 | 尺寸（压缩） | 大小（内存） | |
|---|---|---|---|---|---|
| 2021-05-20 | enwiki_core | en | 1.3GB | 8GB | |
| 2021-05-20 | SimpleWiki_Core | en | 20MB | 130MB | |
| 2021-05-20 | itwiki_core | 它 | 208MB | 1.2GB | |
| 更多... |
Spikex提供了一个命令，可以快捷下下载和安装WikiGraph （Linux或MacOS，Windows尚不支持）：
spikex download-wikigraph simplewiki_core可以从命令行创建WikiGraph ，并指定Wikipedia转储要进行的Wikipedia转储以及在哪里保存：
spikex create-wikigraph
< YOUR-OUTPUT-PATH >
--wiki < WIKI-NAME, default: en >
--version < DUMP-VERSION, default: latest >
--dumps-path < DUMPS-BACKUP-PATH > 然后需要包装和安装：
spikex package-wikigraph
< WIKIGRAPH-RAW-PATH >
< YOUR-OUTPUT-PATH >按照包装过程结束时的说明，并在虚拟环境中安装分发包。现在，您准备按照您的意愿使用Wikigraph：
from spikex . wikigraph import load as wg_load
wg = wg_load ( "enwiki_core" )
page = "Natural_language_processing"
categories = wg . get_categories ( page , distance = 1 )
for category in categories :
print ( category )
> >> Category : Speech_recognition
> >> Category : Artificial_intelligence
> >> Category : Natural_language_processing
> >> Category : Computational_linguistics匹配器与Spacy的相同，但是在一次处理许多模式时（成千上万的订单）时，请更快，因此请在此处遵循官方用法说明。
一个琐碎的例子：
from spikex . matcher import Matcher
from spacy import load as spacy_load
nlp = spacy_load ( "en_core_web_sm" )
matcher = Matcher ( nlp . vocab )
matcher . add ( "TEST" , [[{ "LOWER" : "nlp" }]])
doc = nlp ( "I love NLP" )
for _ , s , e in matcher ( doc ):
print ( doc [ s : e ])
> >> NLP WikiPageX管使用WikiGraph ，以便在匹配Wikipedia页面标题的文本中找到块。
from spacy import load as spacy_load
from spikex . wikigraph import load as wg_load
from spikex . pipes import WikiPageX
nlp = spacy_load ( "en_core_web_sm" )
doc = nlp ( "An apple a day keeps the doctor away" )
wg = wg_load ( "simplewiki_core" )
wpx = WikiPageX ( wg )
doc = wpx ( doc )
for span in doc . _ . wiki_spans :
print ( span . _ . wiki_pages )
> >> [ 'An' ]
> >> [ 'Apple' , 'Apple_(disambiguation)' , 'Apple_(company)' , 'Apple_(tree)' ]
> >> [ 'A' , 'A_(musical_note)' , 'A_(New_York_City_Subway_service)' , 'A_(disambiguation)' , 'A_(Cyrillic)' )]
> >> [ 'Day' ]
> >> [ 'The_Doctor' , 'The_Doctor_(Doctor_Who)' , 'The_Doctor_(Star_Trek)' , 'The_Doctor_(disambiguation)' ]
> >> [ 'The' ]
> >> [ 'Doctor_(Doctor_Who)' , 'Doctor_(Star_Trek)' , 'Doctor' , 'Doctor_(title)' , 'Doctor_(disambiguation)' ] ClusterX管在文本中取用名词块，并使用径向球映射器算法将其簇。
from spacy import load as spacy_load
from spikex . pipes import ClusterX
nlp = spacy_load ( "en_core_web_sm" )
doc = nlp ( "Grab this juicy orange and watch a dog chasing a cat." )
clusterx = ClusterX ( min_score = 0.65 )
doc = clusterx ( doc )
for cluster in doc . _ . cluster_chunks :
print ( cluster )
> >> [ this juicy orange ]
> >> [ a cat , a dog ]Abbrx管在文本中找到缩写和首字母缩写词，将简短和长的形式链接在一起：
from spacy import load as spacy_load
from spikex . pipes import AbbrX
nlp = spacy_load ( "en_core_web_sm" )
doc = nlp ( "a little snippet with an abbreviation (abbr)" )
abbrx = AbbrX ( nlp . vocab )
doc = abbrx ( doc )
for abbr in doc . _ . abbrs :
print ( abbr , "->" , abbr . _ . long_form )
> >> abbr - > abbreviationLabelX管与文本中的图案匹配并标记图案，求解重叠，缩写和缩写词。
from spacy import load as spacy_load
from spikex . pipes import LabelX
nlp = spacy_load ( "en_core_web_sm" )
doc = nlp ( "looking for a computer system engineer" )
patterns = [
[{ "LOWER" : "computer" }, { "LOWER" : "system" }],
[{ "LOWER" : "system" }, { "LOWER" : "engineer" }],
]
labelx = LabelX ( nlp . vocab , [( "TEST" , patterns )], validate = True , only_longest = True )
doc = labelx ( doc )
for labeling in doc . _ . labelings :
print ( labeling , f"[ { labeling . label_ } ]" )
> >> computer system engineer [ TEST ] PhraseX管会创建一个自定义Doc的下划线扩展，该扩展可以通过短语模式的匹配来满足。
from spacy import load as spacy_load
from spikex . pipes import PhraseX
nlp = spacy_load ( "en_core_web_sm" )
doc = nlp ( "I have Melrose and McIntosh apples, or Williams pears" )
patterns = [
[{ "LOWER" : "mcintosh" }],
[{ "LOWER" : "melrose" }],
]
phrasex = PhraseX ( nlp . vocab , "apples" , patterns )
doc = phrasex ( doc )
for apple in doc . _ . apples :
print ( apple )
> >> Melrose
> >> McIntoshSentx管将句子分成文本。它修改了令牌的IS_SENT_START属性，因此必须在Spacy Pipeline中的解析器管道之前添加它：
from spacy import load as spacy_load
from spikex . pipes import SentX
from spikex . defaults import spacy_version
if spacy_version >= 3 :
from spacy . language import Language
@ Language . factory ( "sentx" )
def create_sentx ( nlp , name ):
return SentX ()
nlp = spacy_load ( "en_core_web_sm" )
sentx_pipe = SentX () if spacy_version < 3 else "sentx"
nlp . add_pipe ( sentx_pipe , before = "parser" )
doc = nlp ( "A little sentence. Followed by another one." )
for sent in doc . sents :
print ( sent )
> >> A little sentence .
> >> Followed by another one .随时贡献并获得乐趣！
