如何從元組列表中提取模式元組？

我有以下列表：如何從元組列表中提取模式元組？

data = [('Mr', 'PROPN'), ('.', 'PUNCT'), ('William', 'PROPN'), ('Henry', 'PROPN'), ('Gates', 'PROPN'), (',', 'PUNCT'), ('III', 'NUM'), ('is', 'VERB'), ('Founder', 'PROPN'), ('and', 'CONJ'), ('Technology', 'PROPN'), ('Advisor', 'NOUN'), ('Director', 'NOUN'), ('of', 'ADP'), ('Microsoft', 'PROPN'), ('Corporation', 'PROPN'), ('a', 'DET'), ('cofounder', 'NOUN'), ('served', 'VERB'), ('as', 'ADP'), ('Chairman', 'PROPN'), ('from', 'ADP'), ('our', 'PRON'), ('incorporation', 'NOUN'), ('in', 'ADP'), ('1981', 'NUM'), ('until', 'ADP'), ('2014', 'NUM'), ('He', 'PRON'), ('currently', 'ADV'), ('acts', 'VERB'), ('Technical', 'ADJ'), ('to', 'ADP'), ('Nadella', 'NUM'), ('on', 'ADP'), ('key', 'ADJ'), ('development', 'NOUN'), ('projects', 'NOUN'), ('retired', 'VERB'), ('an', 'DET'), ('employee', 'NOUN'), ('2008', 'NUM'), ('Chief', 'NOUN'), ('Software', 'PROPN'), ('Architect', 'PROPN'), ('2000', 'NUM'), ('2006', 'NUM'), ('when', 'ADV'), ('he', 'PRON'), ('announced', 'VERB'), ('his', 'PRON'), ('two', 'NUM'), ('-', 'PUNCT'), ('year', 'NOUN'), ('plan', 'NOUN'), ('transition', 'VERB'), ('out', 'ADP'), ('day', 'NOUN'), ('full', 'ADJ'), ('time', 'NOUN'), ('role', 'NOUN'), ('Executive', 'PROPN'), ('Officer', 'PROPN'), ('resigned', 'VERB'), ('assumed', 'VERB'), ('the', 'DET'), ('position', 'NOUN'), ('As', 'ADP'), ('co', 'PROPN'), ('chair', 'NOUN'), ('Bill', 'NOUN'), ('&', 'CONJ'), ('Melinda', 'PROPN'), ('Foundation', 'PROPN'), ('shapes', 'NOUN'), ('approves', 'VERB'), ('grant', 'NOUN'), ('making', 'VERB'), ('strategies', 'NOUN'), ('advocates', 'NOUN'), ('for', 'ADP'), ('foundation’s', 'NUM'), ('issues', 'NOUN'), ('helps', 'VERB'), ('set', 'VERB'), ('overall', 'ADJ'), ('direction', 'NOUN'), ('organization', 'NOUN'), ('founder', 'NOUN'), ('’', 'NUM'), ('foresight', 'NOUN'), ('vision', 'NOUN'), ('personal', 'ADJ'), ('computing', 'NOUN'), ('have', 'AUX'), ('been', 'VERB'), ('central', 'ADJ'), ('success', 'NOUN'), ('software', 'NOUN'), ('industry', 'NOUN'), ('has', 'VERB'), ('unparalleled', 'ADJ'), ('knowledge', 'NOUN'), ('Company’s', 'NUM'), ('history', 'NOUN'), ('technologies', 'NOUN'), ('Company', 'NOUN'), ('its', 'PRON'), ('grew', 'VERB'), ('fledgling', 'ADJ'), ('business', 'NOUN'), ('into', 'ADP'), ('world’s', 'NUM'), ('leading', 'VERB'), ('company', 'NOUN'), ('process', 'NOUN'), ('creating', 'VERB'), ('one', 'NUM'), ('most', 'ADV'), ('prolific', 'ADJ'), ('sources', 'NOUN'), ('innovation', 'NOUN'), ('powerful', 'ADJ'), ('brands', 'NOUN'), ('through', 'ADP'), ('motion', 'NOUN'), ('technological', 'ADJ'), ('strategic', 'ADJ'), ('programs', 'NOUN'), ('that', 'DET'), ('are', 'VERB'), ('core', 'NOUN'), ('part', 'NOUN'), ('continues', 'VERB'), ('provide', 'VERB'), ('technical', 'ADJ'), ('input', 'NOUN'), ('evolution', 'NOUN'), ('productivity', 'NOUN'), ('platform', 'NOUN'), ('mobile', 'NOUN'), ('first', 'ADJ'), ('cloud', 'NOUN'), ('world', 'NOUN'), ('His', 'PRON'), ('work', 'NOUN'), ('overseeing', 'VERB'), ('provides', 'VERB'), ('global', 'ADJ'), ('insights', 'NOUN'), ('relevant', 'ADJ'), ('current', 'ADJ'), ('future', 'ADJ'), ('opportunities', 'NOUN'), ('keen', 'ADJ'), ('appreciation', 'NOUN'), ('stakeholder', 'ADJ'), ('interests', 'NOUN')]

我想提取考慮每個元組的第二個元素三合一的模式。例如，假設我想提取所有具有元組之間'of'具有第二元素'NOUN'和'PROPN'元組：

[('Director', 'NOUN'), ('of', 'ADP'), ('Microsoft', 'PROPN')]

因此，我的問題是我如何可以提取不使用正則表達式上面的圖案？。我不想使用正則表達式的原因是，我將開始以更多不同的方式提取元組。例如，元組具有作爲第一值'world’s'其次'VERB'，'NOUN'：

[('world’s', 'NUM'), ('leading', 'VERB'), ('company', 'NOUN')]

來源

2016-12-13 john doe

爲什麼沒有正則表達式？ –

因爲有時寫正則表達式會讓這個模式提取任務更加困難@ElliotRoberts –

如果有多個，應該做什麼？ –

比較快，但可能是不必要的緊湊型解決方案：

from itertools import chain 

# Generator of three-tuples matching requirements: 
# If `data` is large enough that temp `list`s are a problem, might be worth 
# using itertools.islice instead of shallow copy slices 
# or using enumerate with lookaround indexing 
matchtups = (((wd0, tp0), (wd1, tp1), (wd2, tp2)) 
      for (wd0, tp0), (wd1, tp1), (wd2, tp2) in zip(data, data[1:], data[2:]) 
      if wd1 == 'of' and tp0 == 'NOUN' and tp2 == 'PROPN') 

# Flatten out the three-tuple structure: 
results = list(chain.from_iterable(matchtups))

來源

2016-12-13 22:32:45 ShadowRanger

謝謝，這是最有用的。使用類似於詞法分析器或分析器的東西怎麼樣？......您認爲哪種方法最適合完成此任務？ –

另外，如果有更多的模式，比如'[（'Director'，'NOUN'），（''，'ADP'），（'Microsoft'，'PROPN'）]''，怎麼辦？這種方法是否可靠也能抓住它們？ –

@johndoe：我的意思是，如果它有兩個集合，它們將全部出現在一個平坦的列表中，一組接一個匹配（生成器表達式生成離散的三元組，但我解壓縮它們以匹配您的問題的請求輸出）。 – ShadowRanger

你可以遍歷它：

data = [('Mr', 'PROPN'), ('.', 'PUNCT'), ('William', 'PROPN'), ('Henry', 'PROPN'), ('Gates', 'PROPN'), (',', 'PUNCT'), ('III', 'NUM'), ('is', 'VERB'), ('Founder', 'PROPN'), ('and', 'CONJ'), ('Technology', 'PROPN'), ('Advisor', 'NOUN'), ('Director', 'NOUN'), ('of', 'ADP'), ('Microsoft', 'PROPN'), ('Corporation', 'PROPN'), ('a', 'DET'), ('cofounder', 'NOUN'), ('served', 'VERB'), ('as', 'ADP'), ('Chairman', 'PROPN'), ('from', 'ADP'), ('our', 'PRON'), ('incorporation', 'NOUN'), ('in', 'ADP'), ('1981', 'NUM'), ('until', 'ADP'), ('2014', 'NUM'), ('He', 'PRON'), ('currently', 'ADV'), ('acts', 'VERB'), ('Technical', 'ADJ'), ('to', 'ADP'), ('Nadella', 'NUM'), ('on', 'ADP'), ('key', 'ADJ'), ('development', 'NOUN'), ('projects', 'NOUN'), ('retired', 'VERB'), ('an', 'DET'), ('employee', 'NOUN'), ('2008', 'NUM'), ('Chief', 'NOUN'), ('Software', 'PROPN'), ('Architect', 'PROPN'), ('2000', 'NUM'), ('2006', 'NUM'), ('when', 'ADV'), ('he', 'PRON'), ('announced', 'VERB'), ('his', 'PRON'), ('two', 'NUM'), ('-', 'PUNCT'), ('year', 'NOUN'), ('plan', 'NOUN'), ('transition', 'VERB'), ('out', 'ADP'), ('day', 'NOUN'), ('full', 'ADJ'), ('time', 'NOUN'), ('role', 'NOUN'), ('Executive', 'PROPN'), ('Officer', 'PROPN'), ('resigned', 'VERB'), ('assumed', 'VERB'), ('the', 'DET'), ('position', 'NOUN'), ('As', 'ADP'), ('co', 'PROPN'), ('chair', 'NOUN'), ('Bill', 'NOUN'), ('&', 'CONJ'), ('Melinda', 'PROPN'), ('Foundation', 'PROPN'), ('shapes', 'NOUN'), ('approves', 'VERB'), ('grant', 'NOUN'), ('making', 'VERB'), ('strategies', 'NOUN'), ('advocates', 'NOUN'), ('for', 'ADP'), ('foundation’s', 'NUM'), ('issues', 'NOUN'), ('helps', 'VERB'), ('set', 'VERB'), ('overall', 'ADJ'), ('direction', 'NOUN'), ('organization', 'NOUN'), ('founder', 'NOUN'), ('’', 'NUM'), ('foresight', 'NOUN'), ('vision', 'NOUN'), ('personal', 'ADJ'), ('computing', 'NOUN'), ('have', 'AUX'), ('been', 'VERB'), ('central', 'ADJ'), ('success', 'NOUN'), ('software', 'NOUN'), ('industry', 'NOUN'), ('has', 'VERB'), ('unparalleled', 'ADJ'), ('knowledge', 'NOUN'), ('Company’s', 'NUM'), ('history', 'NOUN'), ('technologies', 'NOUN'), ('Company', 'NOUN'), ('its', 'PRON'), ('grew', 'VERB'), ('fledgling', 'ADJ'), ('business', 'NOUN'), ('into', 'ADP'), ('world’s', 'NUM'), ('leading', 'VERB'), ('company', 'NOUN'), ('process', 'NOUN'), ('creating', 'VERB'), ('one', 'NUM'), ('most', 'ADV'), ('prolific', 'ADJ'), ('sources', 'NOUN'), ('innovation', 'NOUN'), ('powerful', 'ADJ'), ('brands', 'NOUN'), ('through', 'ADP'), ('motion', 'NOUN'), ('technological', 'ADJ'), ('strategic', 'ADJ'), ('programs', 'NOUN'), ('that', 'DET'), ('are', 'VERB'), ('core', 'NOUN'), ('part', 'NOUN'), ('continues', 'VERB'), ('provide', 'VERB'), ('technical', 'ADJ'), ('input', 'NOUN'), ('evolution', 'NOUN'), ('productivity', 'NOUN'), ('platform', 'NOUN'), ('mobile', 'NOUN'), ('first', 'ADJ'), ('cloud', 'NOUN'), ('world', 'NOUN'), ('His', 'PRON'), ('work', 'NOUN'), ('overseeing', 'VERB'), ('provides', 'VERB'), ('global', 'ADJ'), ('insights', 'NOUN'), ('relevant', 'ADJ'), ('current', 'ADJ'), ('future', 'ADJ'), ('opportunities', 'NOUN'), ('keen', 'ADJ'), ('appreciation', 'NOUN'), ('stakeholder', 'ADJ'), ('interests', 'NOUN')] 
[(x,y) for x,y in data if ('NOUN' == y) or ('PROPN' in y)]

我把2種方法來評估，如果在一個以上，所以你可以選擇。您也可以使用更強大的語法將其轉換爲pandas來進行查詢。這有助於使用數據框的更復雜的查詢。

import pandas as pd 
data = [('Mr', 'PROPN'), ('.', 'PUNCT'), ('William', 'PROPN'), ('Henry', 'PROPN'), ('Gates', 'PROPN'), (',', 'PUNCT'), ('III', 'NUM'), ('is', 'VERB'), ('Founder', 'PROPN'), ('and', 'CONJ'), ('Technology', 'PROPN'), ('Advisor', 'NOUN'), ('Director', 'NOUN'), ('of', 'ADP'), ('Microsoft', 'PROPN'), ('Corporation', 'PROPN'), ('a', 'DET'), ('cofounder', 'NOUN'), ('served', 'VERB'), ('as', 'ADP'), ('Chairman', 'PROPN'), ('from', 'ADP'), ('our', 'PRON'), ('incorporation', 'NOUN'), ('in', 'ADP'), ('1981', 'NUM'), ('until', 'ADP'), ('2014', 'NUM'), ('He', 'PRON'), ('currently', 'ADV'), ('acts', 'VERB'), ('Technical', 'ADJ'), ('to', 'ADP'), ('Nadella', 'NUM'), ('on', 'ADP'), ('key', 'ADJ'), ('development', 'NOUN'), ('projects', 'NOUN'), ('retired', 'VERB'), ('an', 'DET'), ('employee', 'NOUN'), ('2008', 'NUM'), ('Chief', 'NOUN'), ('Software', 'PROPN'), ('Architect', 'PROPN'), ('2000', 'NUM'), ('2006', 'NUM'), ('when', 'ADV'), ('he', 'PRON'), ('announced', 'VERB'), ('his', 'PRON'), ('two', 'NUM'), ('-', 'PUNCT'), ('year', 'NOUN'), ('plan', 'NOUN'), ('transition', 'VERB'), ('out', 'ADP'), ('day', 'NOUN'), ('full', 'ADJ'), ('time', 'NOUN'), ('role', 'NOUN'), ('Executive', 'PROPN'), ('Officer', 'PROPN'), ('resigned', 'VERB'), ('assumed', 'VERB'), ('the', 'DET'), ('position', 'NOUN'), ('As', 'ADP'), ('co', 'PROPN'), ('chair', 'NOUN'), ('Bill', 'NOUN'), ('&', 'CONJ'), ('Melinda', 'PROPN'), ('Foundation', 'PROPN'), ('shapes', 'NOUN'), ('approves', 'VERB'), ('grant', 'NOUN'), ('making', 'VERB'), ('strategies', 'NOUN'), ('advocates', 'NOUN'), ('for', 'ADP'), ('foundation’s', 'NUM'), ('issues', 'NOUN'), ('helps', 'VERB'), ('set', 'VERB'), ('overall', 'ADJ'), ('direction', 'NOUN'), ('organization', 'NOUN'), ('founder', 'NOUN'), ('’', 'NUM'), ('foresight', 'NOUN'), ('vision', 'NOUN'), ('personal', 'ADJ'), ('computing', 'NOUN'), ('have', 'AUX'), ('been', 'VERB'), ('central', 'ADJ'), ('success', 'NOUN'), ('software', 'NOUN'), ('industry', 'NOUN'), ('has', 'VERB'), ('unparalleled', 'ADJ'), ('knowledge', 'NOUN'), ('Company’s', 'NUM'), ('history', 'NOUN'), ('technologies', 'NOUN'), ('Company', 'NOUN'), ('its', 'PRON'), ('grew', 'VERB'), ('fledgling', 'ADJ'), ('business', 'NOUN'), ('into', 'ADP'), ('world’s', 'NUM'), ('leading', 'VERB'), ('company', 'NOUN'), ('process', 'NOUN'), ('creating', 'VERB'), ('one', 'NUM'), ('most', 'ADV'), ('prolific', 'ADJ'), ('sources', 'NOUN'), ('innovation', 'NOUN'), ('powerful', 'ADJ'), ('brands', 'NOUN'), ('through', 'ADP'), ('motion', 'NOUN'), ('technological', 'ADJ'), ('strategic', 'ADJ'), ('programs', 'NOUN'), ('that', 'DET'), ('are', 'VERB'), ('core', 'NOUN'), ('part', 'NOUN'), ('continues', 'VERB'), ('provide', 'VERB'), ('technical', 'ADJ'), ('input', 'NOUN'), ('evolution', 'NOUN'), ('productivity', 'NOUN'), ('platform', 'NOUN'), ('mobile', 'NOUN'), ('first', 'ADJ'), ('cloud', 'NOUN'), ('world', 'NOUN'), ('His', 'PRON'), ('work', 'NOUN'), ('overseeing', 'VERB'), ('provides', 'VERB'), ('global', 'ADJ'), ('insights', 'NOUN'), ('relevant', 'ADJ'), ('current', 'ADJ'), ('future', 'ADJ'), ('opportunities', 'NOUN'), ('keen', 'ADJ'), ('appreciation', 'NOUN'), ('stakeholder', 'ADJ'), ('interests', 'NOUN')] 
data = pd.DataFrame(data, columns=['word','type']) 
data[(data.type=='NOUN') | (data.type=='PROPN')]

編的評論部分：

你必須瞭解你的數據一樣的東西的能力。

data.groupby(data.type).count() 

     word 
type 
ADJ  20 
ADP  12 
ADV  3 
AUX  1 
CONJ  2 
DET  4 
NOUN  56 
NUM  13 
PRON  6 
PROPN 16 
PUNCT  3 
VERB  22

您可以在計算完成後將其轉換回python數據類型。

list(data[(data.type=='NOUN') | (data.type=='PROPN')].word)

來源

2016-12-13 22:30:51 Back2Basics

我不知道這可以用熊貓來完成。你能提供更多關於如何使用熊貓來完成這個任務的例子嗎？非常感謝你。 –

像這樣嗎？

thing_list = [] 
for i, x in enumerate(data): 
    if x[0] == "of": 
     if (data[i-1][1] == "NOUN") and (data[i+1][1] == "PROPN"): 
      thing_list.append(data[i-1:i+2])

來源

2016-12-13 22:34:00

我得到了這個：'[[（'Director'，'NOUN'），（''，'ADP'）]]'這是錯誤的。 –

哎呀，混淆了一些痕跡。我認爲現在已經解決了。 –

因爲看起來很有意思而在這件事上做了一個破解。如果你願意忍受的map S，lambda S和filter湯s此似乎工作：

matches = map(
    lambda _: (data[_ - 1], data[_], data[_ + 1]), 
    filter(
     lambda _: data[_ - 1][1] == "NOUN" and data[_ + 1][1] == "PROPN", 
     map(
      lambda _: _[0], 
      filter(
       lambda _: _[1][0] == "of", 
       enumerate(data) 
      ) 
     ) 
    ) 
)

來源

2016-12-13 22:41:59 joeb

如果您需要'lambda'來使用'map'或'filter'，請不要使用'map'或'filter'。它比等效的listcomps或genexprs慢，不易讀，不太明顯，並且通常不那麼簡潔。 – ShadowRanger

def trioPattern(trioCols, trioElements): 
    """trioCols = (Use element 0 or 1 of the first pair, 0 or 1 of the second pair, 0 or 1 of the third pair) 
     trioElements = (Phrase of the first element, Phrase of the second element, Phrase of the third element)""" 

    data = [('Mr', 'PROPN'), ('.', 'PUNCT'), ('William', 'PROPN'), ('Henry', 'PROPN'), ('Gates', 'PROPN'), (',', 'PUNCT'), ('III', 'NUM'), ('is', 'VERB'), ('Founder', 'PROPN'), ('and', 'CONJ'), ('Technology', 'PROPN'), ('Advisor', 'NOUN'), ('Director', 'NOUN'), ('of', 'ADP'), ('Microsoft', 'PROPN'), ('Corporation', 'PROPN'), ('a', 'DET'), ('cofounder', 'NOUN'), ('served', 'VERB'), ('as', 'ADP'), ('Chairman', 'PROPN'), ('from', 'ADP'), ('our', 'PRON'), ('incorporation', 'NOUN'), ('in', 'ADP'), ('1981', 'NUM'), ('until', 'ADP'), ('2014', 'NUM'), ('He', 'PRON'), ('currently', 'ADV'), ('acts', 'VERB'), ('Technical', 'ADJ'), ('to', 'ADP'), ('Nadella', 'NUM'), ('on', 'ADP'), ('key', 'ADJ'), ('development', 'NOUN'), ('projects', 'NOUN'), ('retired', 'VERB'), ('an', 'DET'), ('employee', 'NOUN'), ('2008', 'NUM'), ('Chief', 'NOUN'), ('Software', 'PROPN'), ('Architect', 'PROPN'), ('2000', 'NUM'), ('2006', 'NUM'), ('when', 'ADV'), ('he', 'PRON'), ('announced', 'VERB'), ('his', 'PRON'), ('two', 'NUM'), ('-', 'PUNCT'), ('year', 'NOUN'), ('plan', 'NOUN'), ('transition', 'VERB'), ('out', 'ADP'), ('day', 'NOUN'), ('full', 'ADJ'), ('time', 'NOUN'), ('role', 'NOUN'), ('Executive', 'PROPN'), ('Officer', 'PROPN'), ('resigned', 'VERB'), ('assumed', 'VERB'), ('the', 'DET'), ('position', 'NOUN'), ('As', 'ADP'), ('co', 'PROPN'), ('chair', 'NOUN'), ('Bill', 'NOUN'), ('&', 'CONJ'), ('Melinda', 'PROPN'), ('Foundation', 'PROPN'), ('shapes', 'NOUN'), ('approves', 'VERB'), ('grant', 'NOUN'), ('making', 'VERB'), ('strategies', 'NOUN'), ('advocates', 'NOUN'), ('for', 'ADP'), ('foundation’s', 'NUM'), ('issues', 'NOUN'), ('helps', 'VERB'), ('set', 'VERB'), ('overall', 'ADJ'), ('direction', 'NOUN'), ('organization', 'NOUN'), ('founder', 'NOUN'), ('’', 'NUM'), ('foresight', 'NOUN'), ('vision', 'NOUN'), ('personal', 'ADJ'), ('computing', 'NOUN'), ('have', 'AUX'), ('been', 'VERB'), ('central', 'ADJ'), ('success', 'NOUN'), ('software', 'NOUN'), ('industry', 'NOUN'), ('has', 'VERB'), ('unparalleled', 'ADJ'), ('knowledge', 'NOUN'), ('Company’s', 'NUM'), ('history', 'NOUN'), ('technologies', 'NOUN'), ('Company', 'NOUN'), ('its', 'PRON'), ('grew', 'VERB'), ('fledgling', 'ADJ'), ('business', 'NOUN'), ('into', 'ADP'), ('world’s', 'NUM'), ('leading', 'VERB'), ('company', 'NOUN'), ('process', 'NOUN'), ('creating', 'VERB'), ('one', 'NUM'), ('most', 'ADV'), ('prolific', 'ADJ'), ('sources', 'NOUN'), ('innovation', 'NOUN'), ('powerful', 'ADJ'), ('brands', 'NOUN'), ('through', 'ADP'), ('motion', 'NOUN'), ('technological', 'ADJ'), ('strategic', 'ADJ'), ('programs', 'NOUN'), ('that', 'DET'), ('are', 'VERB'), ('core', 'NOUN'), ('part', 'NOUN'), ('continues', 'VERB'), ('provide', 'VERB'), ('technical', 'ADJ'), ('input', 'NOUN'), ('evolution', 'NOUN'), ('productivity', 'NOUN'), ('platform', 'NOUN'), ('mobile', 'NOUN'), ('first', 'ADJ'), ('cloud', 'NOUN'), ('world', 'NOUN'), ('His', 'PRON'), ('work', 'NOUN'), ('overseeing', 'VERB'), ('provides', 'VERB'), ('global', 'ADJ'), ('insights', 'NOUN'), ('relevant', 'ADJ'), ('current', 'ADJ'), ('future', 'ADJ'), ('opportunities', 'NOUN'), ('keen', 'ADJ'), ('appreciation', 'NOUN'), ('stakeholder', 'ADJ'), ('interests', 'NOUN')] 

    #Elements of the triple pattern 
    ColE1, ColE2, ColE3 = trioCols 
    trios = dict([((data[e][ColE1], data[e+1][ColE2], data[e+2][ColE3]), (data[e], data[e+1], data[e+2])) for e in range(0, len(data)-2)]) 

    #Triple pattern phrases 
    E1, E2, E3 = trioElements 
    if trios.has_key((E1, E2, E3)): 
     return trios[(E1, E2, E3)] 
    else: 
     return "Not found"

例子：

trioPattern（（1,0,1））

(('Director', 'NOUN'), ('of', 'ADP'), ('Microsoft', 'PROPN'))

trioPattern（（0,1,1），（「世界」，「動詞」，「名詞」）），（「PROPN」「的」「名詞」，）

(('world’s', 'NUM'), ('leading', 'VERB'), ('company', 'NOUN'))

來源

2016-12-13 23:07:08 JCA

如何從元組列表中提取模式元組？

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