spikex
1.0.0
Spikex是一系列準備插入Spacy管道的管道。它旨在幫助以幾乎零的精力來構建知識提取工具。
威克拉普從未如此快速閃電：
Doc的下劃線擴展。示例是nounphrasex和verbphrasex ，分別提取名詞短語和動詞短語一些要求是從Spacy繼承的：
一些依賴項使用Cython ，需要在Spikex之前安裝它：
pip install cython請記住，始終建議使用虛擬環境，以避免修改系統狀態。
此時，安裝Spikex通過PIP是一行命令：
pip install spikexSpikex管與Spacy一起使用，因此需要安裝它的模型。在這裡遵循官方指示。支持全新的Spacy 3.0！
WikiGraph是由Wikipedia的一些關鍵組成部分開始構建的：頁面，類別及其之間的關係。
根據維基百科垃圾場的大小，創建WikiGraph可能需要時間。因此，我們提供準備使用的Wikigraphs：
| 日期 | 威克拉普 | 朗 | 尺寸（壓縮） | 大小（內存） | |
|---|---|---|---|---|---|
| 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提供了一個命令，可以快捷下下載和安裝WikiGraph （Linux或MacOS，Windows尚不支持）：
spikex download-wikigraph simplewiki_core可以從命令行創建WikiGraph ，並指定Wikipedia轉儲要進行的Wikipedia轉儲以及在哪裡保存：
spikex create-wikigraph
< YOUR-OUTPUT-PATH >
--wiki < WIKI-NAME, default: en >
--version < DUMP-VERSION, default: latest >
--dumps-path < DUMPS-BACKUP-PATH > 然後需要包裝和安裝：
spikex package-wikigraph
< WIKIGRAPH-RAW-PATH >
< YOUR-OUTPUT-PATH >按照包裝過程結束時的說明，並在虛擬環境中安裝分發包。現在，您準備按照您的意願使用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匹配器與Spacy的相同，但是在一次處理許多模式時（成千上萬的訂單）時，請更快，因此請在此處遵循官方用法說明。
一個瑣碎的例子：
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頁面標題的文本中找到塊。
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管在文本中取用名詞塊，並使用徑向球映射器算法將其簇。
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管在文本中找到縮寫和首字母縮寫詞，將簡短和長的形式鏈接在一起：
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管與文本中的圖案匹配並標記圖案，求解重疊，縮寫和縮寫詞。
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管會創建一個自定義Doc的下劃線擴展，該擴展可以通過短語模式的匹配來滿足。
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管將句子分成文本。它修改了令牌的IS_SENT_START屬性，因此必須在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 .隨時貢獻並獲得樂趣！
