宣告：程式碼的執行環境為Python3。Python3與Python2在一些細節上會有所不同，希望廣大讀者注意。本部落格以程式碼為主，程式碼中會有詳細的註釋。相關文章將會發布在我的個人部落格專欄《Python自然語言處理》，歡迎大家關注。

一、古騰堡語料庫

# 古騰堡語料庫
from nltk.corpus import gutenberg  # 載入古騰堡語料庫
gutenberg.fileids()

Out[2]: 
['austen-emma.txt',
 'austen-persuasion.txt',
 'austen-sense.txt',
 'bible-kjv.txt',
 'blake-poems.txt',
 'bryant-stories.txt',
 'burgess-busterbrown.txt',
 'carroll-alice.txt',
 'chesterton-ball.txt',
 'chesterton-brown.txt',
 'chesterton-thursday.txt',
 'edgeworth-parents.txt',
 'melville-moby_dick.txt',
 'milton-paradise.txt',
 'shakespeare-caesar.txt',
 'shakespeare-hamlet.txt',
 'shakespeare-macbeth.txt',
 'whitman-leaves.txt']

 

emma = gutenberg.words('austen-emma.txt')  # 載入austen-emma.txt這個檔案
print(emma)

['[', 'Emma', 'by', 'Jane', 'Austen', '1816', ']', ...]

for fileid in gutenberg.fileids():
     num_chars = len(gutenberg.raw(fileid))  # 以字元為單位，獲取文字的長度
     num_words = len(gutenberg.words(fileid))  # 以單詞為單位，獲取文字的長度
     num_sents = len(gutenberg.sents(fileid))  # 以句子為單位，獲取文字的長度
     num_vocab = len(set([w.lower() for w in gutenberg.words(fileid)]))  # 獲取文字中不重複的單詞個數
     print(num_chars, num_words, num_sents, num_vocab, fileid)
     
887071 192427 7752 7344 austen-emma.txt
466292 98171 3747 5835 austen-persuasion.txt
673022 141576 4999 6403 austen-sense.txt
4332554 1010654 30103 12767 bible-kjv.txt
38153 8354 438 1535 blake-poems.txt
249439 55563 2863 3940 bryant-stories.txt
84663 18963 1054 1559 burgess-busterbrown.txt
144395 34110 1703 2636 carroll-alice.txt
457450 96996 4779 8335 chesterton-ball.txt
406629 86063 3806 7794 chesterton-brown.txt
320525 69213 3742 6349 chesterton-thursday.txt
935158 210663 10230 8447 edgeworth-parents.txt
1242990 260819 10059 17231 melville-moby_dick.txt
468220 96825 1851 9021 milton-paradise.txt
112310 25833 2163 3032 shakespeare-caesar.txt
162881 37360 3106 4716 shakespeare-hamlet.txt
100351 23140 1907 3464 shakespeare-macbeth.txt
711215 154883 4250 12452 whitman-leaves.txt
 

macbeth_sentences = gutenberg.sents('shakespeare-macbeth.txt')  # 以句子為單位獲取文字內容
print(macbeth_sentences)

[['[', 'The', 'Tragedie', 'of', 'Macbeth', 'by', 'William', 'Shakespeare', '1603', ']'], ['Actus', 'Primus', '.'], ...]



print(macbeth_sentences[1037])  # 獲取下標為1037的句子

['Good', 'night', ',', 'and', 'better', 'health', 'Attend', 'his', 'Maiesty']



longest_len = max([len(s) for s in macbeth_sentences])  # 獲取最長句子的長度
print(longest_len)

158



print([s for s in macbeth_sentences if len(s) == longest_len])  # 打印出最長的句子

[['Doubtfull', 'it', 'stood', ',', 'As', 'two', 'spent', 'Swimmers', ',', 'that', 'doe', 'cling', 'together', ',', 'And', 'choake', 'their', 'Art', ':', 'The', 'mercilesse', 'Macdonwald', '(', 'Worthie', 'to', 'be', 'a', 'Rebell', ',', 'for', 'to', 'that', 'The', 'multiplying', 'Villanies', 'of', 'Nature', 'Doe', 'swarme', 'vpon', 'him', ')', 'from', 'the', 'Westerne', 'Isles', 'Of', 'Kernes', 'and', 'Gallowgrosses', 'is', 'supply', "'", 'd', ',', 'And', 'Fortune', 'on', 'his', 'damned', 'Quarry', 'smiling', ',', 'Shew', "'", 'd', 'like', 'a', 'Rebells', 'Whore', ':', 'but', 'all', "'", 's', 'too', 'weake', ':', 'For', 'braue', 'Macbeth', '(', 'well', 'hee', 'deserues', 'that', 'Name', ')', 'Disdayning', 'Fortune', ',', 'with', 'his', 'brandisht', 'Steele', ',', 'Which', 'smoak', "'", 'd', 'with', 'bloody', 'execution', '(', 'Like', 'Valours', 'Minion', ')', 'caru', "'", 'd', 'out', 'his', 'passage', ',', 'Till', 'hee', 'fac', "'", 'd', 'the', 'Slaue', ':', 'Which', 'neu', "'", 'r', 'shooke', 'hands', ',', 'nor', 'bad', 'farwell', 'to', 'him', ',', 'Till', 'he', 'vnseam', "'", 'd', 'him', 'from', 'the', 'Naue', 'toth', "'", 'Chops', ',', 'And', 'fix', "'", 'd', 'his', 'Head', 'vpon', 'our', 'Battlements']]
 

二、網路和聊天文字語料庫

# 網路和聊天文字
from nltk.corpus import webtext  # 載入語料庫
webtext.fileids()
for fileid in webtext.fileids():  # 獲取文字內容
     print(fileid, webtext.raw(fileid)[:65], '...')
     
firefox.txt Cookie Manager: "Don't allow sites that set removed cookies to se ...
grail.txt SCENE 1: [wind] [clop clop clop] 
KING ARTHUR: Whoa there!  [clop ...
overheard.txt White guy: So, do you have any plans for this evening?
Asian girl ...
pirates.txt PIRATES OF THE CARRIBEAN: DEAD MAN'S CHEST, by Ted Elliott & Terr ...
singles.txt 25 SEXY MALE, seeks attrac older single lady, for discreet encoun ...
wine.txt Lovely delicate, fragrant Rhone wine. Polished leather and strawb ...

from nltk.corpus import nps_chat  # 獲取聊天室語料庫
chatroom = nps_chat.posts('10-19-20s_706posts.xml')
print(chatroom[123])
['i', 'do', "n't", 'want', 'hot', 'pics', 'of', 'a', 'female', ',', 'I', 'can', 'look', 'in', 'a', 'mirror', '.']

三、布朗語料庫

from nltk.corpus import brown
brown.categories()  # 檢視布朗語料庫的分類
Out[11]: 
['adventure',
 'belles_lettres',
 'editorial',
 'fiction',
 'government',
 'hobbies',
 'humor',
 'learned',
 'lore',
 'mystery',
 'news',
 'religion',
 'reviews',
 'romance',
 'science_fiction']

brown.words(categories='news')  # 檢視news分類裡面詞連結串列的結果

Out[12]: ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]



brown.words(fileids=['cg22'])  # 檢視cg22文本里面的詞連結串列結果

Out[13]: ['Does', 'our', 'society', 'have', 'a', 'runaway', ',', ...]



brown.sents(categories=['news', 'editorial', 'reviews'])  # 展示多個分類的內容

Out[14]: [['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', 'Friday', 'an', 'investigation', 'of', "Atlanta's", 'recent', 'primary', 'election', 'produced', '``', 'no', 'evidence', "''", 'that', 'any', 'irregularities', 'took', 'place', '.'], ['The', 'jury', 'further', 'said', 'in', 'term-end', 'presentments', 'that', 'the', 'City', 'Executive', 'Committee', ',', 'which', 'had', 'over-all', 'charge', 'of', 'the', 'election', ',', '``', 'deserves', 'the', 'praise', 'and', 'thanks', 'of', 'the', 'City', 'of', 'Atlanta', "''", 'for', 'the', 'manner', 'in', 'which', 'the', 'election', 'was', 'conducted', '.'], ...]

import nltk
news_text = brown.words(categories='news')  # 儲存news分類的結果
fdist = nltk.FreqDist([w.lower() for w in news_text])  # 將單詞轉換為小寫，統計詞頻
modals = ['can', 'could', 'may', 'might', 'must', 'will']  # 定義一個情態動詞列表
for m in modals:  # 統計一下情態動詞的詞頻數量
    print(m + ':', fdist[m])
    
can: 94
could: 87
may: 93
might: 38
must: 53
will: 389

cfd = nltk.ConditionalFreqDist(  # 條件頻率分佈函式
     (genre, word)
     for genre in brown.categories()  #文體
     for word in brown.words(categories=genre))  # 單詞
genres = ['news', 'religion', 'hobbies', 'science_fiction', 'romance', 'humor']
modals = ['can', 'could', 'may', 'might', 'must', 'will']
cfd.tabulate(conditions=genres, samples=modals)  # 獲取一個交叉圖

                  can could   may might  must  will 
           news    93    86    66    38    50   389 
       religion    82    59    78    12    54    71 
        hobbies   268    58   131    22    83   264 
science_fiction    16    49     4    12     8    16 
        romance    74   193    11    51    45    43 
          humor    16    30     8     8     9    13 

四、路透社語料庫

from nltk.corpus import reuters
print(len(reuters.fileids()))  # 文件的數目
print(len(reuters.categories()))  # 分類數目

10788
90

reuters.categories('training/9865')  # 支援單個搜尋和列表搜尋

Out[20]: ['barley', 'corn', 'grain', 'wheat']



reuters.categories(['training/9865', 'training/9880'])

Out[21]: ['barley', 'corn', 'grain', 'money-fx', 'wheat']

五、就職演講語料庫

#就職演講語料庫
from nltk.corpus import inaugural
inaugural.fileids()
print([fileid[:4] for fileid in inaugural.fileids()])  # 打印出語料庫中每個檔名的前四個字元
['1789', '1793', '1797', '1801', '1805', '1809', '1813', '1817', '1821', '1825', '1829', '1833', '1837', '1841', '1845', '1849', '1853', '1857', '1861', '1865', '1869', '1873', '1877', '1881', '1885', '1889', '1893', '1897', '1901', '1905', '1909', '1913', '1917', '1921', '1925', '1929', '1933', '1937', '1941', '1945', '1949', '1953', '1957', '1961', '1965', '1969', '1973', '1977', '1981', '1985', '1989', '1993', '1997', '2001', '2005', '2009']

cfd = nltk.ConditionalFreqDist(  # 檢視'america'和'citizen'兩個單詞在隨著時間的推移在文章中出現的頻數
    (target, fileid[:4]) 
    for fileid in inaugural.fileids()
    for w in inaugural.words(fileid)
    for target in ['america', 'citizen'] 
    if w.lower().startswith(target))
cfd.plot()

影象如下：

六、其他語料庫（不同語言的語料庫）

nltk.corpus.cess_esp.words()

Out[25]: ['El', 'grupo', 'estatal', 'Electricité_de_France', ...]



nltk.corpus.floresta.words()

Out[26]: ['Um', 'revivalismo', 'refrescante', 'O', '7_e_Meio', ...]



nltk.corpus.indian.words('hindi.pos')

Out[27]: ['पूर्ण', 'प्रतिबंध', 'हटाओ', ':', 'इराक', 'संयुक्त', ...]



nltk.corpus.udhr.fileids()

Out[28]: 
['Abkhaz-Cyrillic+Abkh',
 'Abkhaz-UTF8',
 'Achehnese-Latin1',
 'Achuar-Shiwiar-Latin1',
 'Adja-UTF8',
 'Afaan_Oromo_Oromiffa-Latin1',
 'Afrikaans-Latin1',
 'Aguaruna-Latin1',
 'Akuapem_Twi-UTF8',
 'Albanian_Shqip-Latin1',
 'Amahuaca',
 'Amahuaca-Latin1',
 'Amarakaeri-Latin1',
 'Amuesha-Yanesha-UTF8',
  ...


print(nltk.corpus.udhr.words('Javanese-Latin1')[11:])

['Saben', 'umat', 'manungsa', 'lair', 'kanthi', 'hak', ...]

# 條件頻率分佈的例子
from nltk.corpus import udhr
languages = ['Chickasaw', 'English', 'German_Deutsch',
    'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']
cfd = nltk.ConditionalFreqDist(
    (lang, len(word))
    for lang in languages
    for word in udhr.words(lang + '-Latin1'))
cfd.plot(cumulative=True)

影象如下：

七、載入自己的語料庫

from nltk.corpus import PlaintextCorpusReader
corpus_root = 'F:/icwb2-data/training'  #檔案目錄
wordlists = PlaintextCorpusReader(corpus_root, ['pku_training.utf8','cityu_training.utf8','msr_training.utf8','pku_training.utf8']) 
wordlists.fileids()

Out[31]: 
['pku_training.utf8',
 'cityu_training.utf8',
 'msr_training.utf8',
 'pku_training.utf8']

# 打印出相關的資訊
print(len(wordlists.words('pku_training.utf8')))

1119084



print(len(wordlists.sents('pku_training.utf8')))

4

語料庫的基本函式：

八、條件頻率分佈

1、條件與事件

'''
text = ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said',...]
pairs = [('news', 'The'), ('news', 'Fulton'), ('news', 'County'),...]
'''

2、按文字計算詞頻

genre_word = [(genre, word) 
    for genre in ['news', 'romance'] 
    for word in brown.words(categories=genre)] 
len(genre_word)

Out[35]: 170576



print(genre_word[:4])

[('news', 'The'), ('news', 'Fulton'), ('news', 'County'), ('news', 'Grand')]

nltk.ConditionalFreqDist(genre_word).conditions()  # 顯示文體

Out[37]: ['news', 'romance']



print(nltk.ConditionalFreqDist(genre_word)['news'])

<FreqDist with 14394 samples and 100554 outcomes>



print(nltk.ConditionalFreqDist(genre_word)['news']['could'])  # 某個條件下，某個特定詞語出現的次數

86

3、繪製分佈圖和分佈表

from nltk.corpus import inaugural
cfd2 = nltk.ConditionalFreqDist(
    (target, fileid[:4])
    for fileid in inaugural.fileids()
    for w in inaugural.words(fileid)
    for target in ['america', 'citizen']
    if w.lower().startswith(target))
cfd2.plot()

圖形如下：

from nltk.corpus import udhr
languages = ['Chickasaw', 'English', 'German_Deutsch',
             'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']
udhr.fileids()
cfd3 = nltk.ConditionalFreqDist(
    (lang, len(word))
    for lang in languages
    for word in udhr.words(lang + '-Latin1'))
cfd3.tabulate(conditions=['English', 'German_Deutsch'],
              samples=range(10), cumulative=True) 
 
                  0    1    2    3    4    5    6    7    8    9 
       English    0  185  525  883  997 1166 1283 1440 1558 1638 
German_Deutsch    0  171  263  614  717  894 1013 1110 1213 1275 

4、使用雙鏈詞生成隨機文字

def generate_model(cfdist, word, num=15):
    for i in range(num):
        print(word)
        word = cfdist[word].max()  # 選擇這個詞語之後最大可能出現的詞語
text = nltk.corpus.genesis.words('english-kjv.txt')
bigrams = nltk.bigrams(text)
cfd4 = nltk.ConditionalFreqDist(bigrams)
print(list(cfd4['living']))

['creature', 'thing', 'soul', '.', 'substance', ',']



generate_model(cfd4, 'living')

living
creature
that
he
said
,
and
the
land
of
the
land
of
the
land

常用的條件頻率分佈方法：

九、詞典

1、詞彙列表語料庫

def unusual_words(text):  # 定義一個函式，找出文字中不常見的詞語
    text_vocab = set(w.lower() for w in text if w.isalpha())
    english_vocab = set(w.lower() for w in nltk.corpus.words.words())
    unusual = text_vocab.difference(english_vocab)  # 計算差集
    return sorted(unusual)  # 打印出差集詞語
unusual_words(nltk.corpus.gutenberg.words('austen-sense.txt'))  # 應用到古騰堡語料庫中

Out[46]: 
['abbeyland',
 'abhorred',
 'abilities',
 'abounded',
 'abridgement',
 'abused',
 'abuses',
 'accents',
 'accepting',
 'accommodations',
 'accompanied',
 'accounted',
 'altered',
 'altering',
 'amended',
 'amounted',
 'amusements',
 'ankles',
 'annamaria',
 'annexed',
 'announced',
 'announcing',
 'annuities',
  ...

from nltk.corpus import stopwords  # 停用詞語料庫
stopwords.words('english')  # 列舉出english中的停用詞

Out[48]: 
['i',
 'me',
 'my',
 'myself',
 'we',
 'our',
 'ours',
 'ourselves',
 'you',
 "you're",
 "you've",
 "you'll",
 "you'd",
 'your',
 'yours',
 'yourself',
  ...

def content_fraction(text):  # 獲取非停用詞在列表中所佔的比例
    stopwords = nltk.corpus.stopwords.words('english')
    content = [w for w in text if w.lower() not in stopwords]
    return len(content) / len(text)
content_fraction(nltk.corpus.reuters.words())

Out[49]: 0.735240435097661

names = nltk.corpus.names  # 姓名詞彙表
names.fileids()

Out[51]: ['female.txt', 'male.txt']

male_names = names.words('male.txt')
female_names = names.words('female.txt')
manAndWoman = [w for w in male_names if w in female_names]
print(manAndWoman)  # 檢視男女通用的姓名有哪些

['Abbey', 'Abbie', 'Abby', 'Addie', 'Adrian', 'Adrien', 'Ajay', 'Alex', 'Alexis', 'Alfie', 'Ali', 'Alix', 'Allie', 'Allyn', 'Andie', 'Andrea', 'Andy', 'Angel', 'Angie', 'Ariel', 'Ashley', 'Aubrey', 'Augustine', 'Austin', 'Averil', 'Barrie', 'Barry', 'Beau', 'Bennie', 'Benny', 'Bernie', 'Bert', 'Bertie', 'Bill', 'Billie', 'Billy', 'Blair', 'Blake', 'Bo', 'Bobbie', 'Bobby', 'Brandy', 'Brett', 'Britt',...]

cfd5 = nltk.ConditionalFreqDist(  # 獲取名字的最後一個字母
    (fileid, name[-1])
    for fileid in names.fileids()
    for name in names.words(fileid))
cfd5.plot()

影象如下：

2、發音詞典

entries = nltk.corpus.cmudict.entries()
len(entries)

Out[54]: 133737

for entry in entries[39943:39951]:  # 打印出一部分發音詞典的內容
    print(entry)
    
('explorer', ['IH0', 'K', 'S', 'P', 'L', 'AO1', 'R', 'ER0'])
('explorers', ['IH0', 'K', 'S', 'P', 'L', 'AO1', 'R', 'ER0', 'Z'])
('explores', ['IH0', 'K', 'S', 'P', 'L', 'AO1', 'R', 'Z'])
('exploring', ['IH0', 'K', 'S', 'P', 'L', 'AO1', 'R', 'IH0', 'NG'])
('explosion', ['IH0', 'K', 'S', 'P', 'L', 'OW1', 'ZH', 'AH0', 'N'])
('explosions', ['IH0', 'K', 'S', 'P', 'L', 'OW1', 'ZH', 'AH0', 'N', 'Z'])

for word, pron in entries:   # 找出三音素的詞語，開頭是p，結尾是t
    if len(pron) == 3: 
        ph1, ph2, ph3 = pron 
        if ph1 == 'P' and ph3 == 'T':
            print(word, ph2)
            
pait EY1
pat AE1
pate EY1
patt AE1
peart ER1
...

def stress(pron):  # 返回重音的情況
    return [char for phone in pron for char in phone if char.isdigit()]
print([w for w, pron in entries if stress(pron) == ['0', '1', '0', '2', '0']])  # 重音在第二個元素，次重音在第四個元素

['abbreviated', 'abbreviated', 'abbreviating', 'accelerated', 'accelerating', 'accelerator', 'accelerators', 'accentuated', 'accentuating', 'accommodated', 'accommodating', 'accommodative', 'accumulated', 'accumulating', 'accumulative', 'accumulator', 'accumulators', 'accusatory',...]

3、比較詞典

from nltk.corpus import swadesh
swadesh.fileids()
Out[58]: 
['be',
 'bg',
 'bs',
 'ca',
 'cs',
 'cu',
 ...

fr2en = swadesh.entries(['fr', 'en'])  # 英語和法語同義詞匹配
print(fr2en)

[('je', 'I'), ('tu, vous', 'you (singular), thou'), ('il', 'he'), ('nous', 'we'), ('vous', 'you (plural)'), ('ils, elles', 'they'), ('ceci', 'this'), ('cela', 'that'), ('ici', 'here'), ('là', 'there'), ('qui', 'who'), ('quoi', 'what'), ('où', 'where'), ('quand', 'when'), ('comment', 'how'), ...]

de2en = swadesh.entries(['de', 'en']) # German-English
es2en = swadesh.entries(['es', 'en']) # Spanish-English
translate.update(dict(de2en))
translate.update(dict(es2en))
print(translate['Hund'])

dog



print(translate['perro'])

dog

languages = ['en', 'de', 'nl', 'es', 'fr', 'pt', 'la']
for i in [139, 140, 141, 142]:  # 比較德語和拉丁語的差別
    print(swadesh.entries(languages)[i])
    
('say', 'sagen', 'zeggen', 'decir', 'dire', 'dizer', 'dicere')
('sing', 'singen', 'zingen', 'cantar', 'chanter', 'cantar', 'canere')
('play', 'spielen', 'spelen', 'jugar', 'jouer', 'jogar, brincar', 'ludere')
('float', 'schweben', 'zweven', 'flotar', 'flotter', 'flutuar, boiar', 'fluctuare')

4、詞彙工具

from nltk.corpus import toolbox
toolbox.entries('rotokas.dic')

Out[65]: 
[('kaa',
  [('ps', 'V'),
   ('pt', 'A'),
   ('ge', 'gag'),
   ('tkp', 'nek i pas'),
   ('dcsv', 'true'),
   ('vx', '1'),
   ('sc', '???'),
   ('dt', '29/Oct/2005'),
  ...

十、Wordnet

1、同義詞

from nltk.corpus import wordnet as wn
wn.synsets('motorcar')  # 找到與motorcar同義的詞

Out[66]: [Synset('car.n.01')]



wn.synset('car.n.01').lemma_names()  # 檢視car.n.01裡面的內容

Out[67]: ['car', 'auto', 'automobile', 'machine', 'motorcar']



wn.synset('car.n.01').definition()  # 定義

Out[68]: 'a motor vehicle with four wheels; usually propelled by an internal combustion engine'



wn.synset('car.n.01').examples()  # 例子

Out[69]: ['he needs a car to get to work']

wn.synset('car.n.01').lemmas()

Out[70]: 
[Lemma('car.n.01.car'),
 Lemma('car.n.01.auto'),
 Lemma('car.n.01.automobile'),
 Lemma('car.n.01.machine'),
 Lemma('car.n.01.motorcar')]

wn.lemma('car.n.01.automobile')

Out[71]: Lemma('car.n.01.automobile')


wn.lemma('car.n.01.automobile').synset()

Out[72]: Synset('car.n.01')


wn.lemma('car.n.01.automobile').name()

Out[73]: 'automobile'

wn.synsets('car')

Out[74]: 
[Synset('car.n.01'),
 Synset('car.n.02'),
 Synset('car.n.03'),
 Synset('car.n.04'),
 Synset('cable_car.n.01')]

motorcar = wn.synset('car.n.01')
types_of_motorcar = motorcar.hyponyms()  # 獲取下位詞級
print(types_of_motorcar[26])

Synset('stanley_steamer.n.01')


sorted([lemma.name() for synset in types_of_motorcar for lemma in synset.lemmas()])

Out[78]: 
['Model_T',
 'S.U.V.',
 'SUV',
 'Stanley_Steamer',
 'ambulance',
 'beach_waggon',
 'beach_wagon',
 'bus',
 'cab',
 'compact',

2、其他詞彙關係

wn.synset('tree.n.01').part_meronyms()  # 樹的部分

Out[79]: 
[Synset('burl.n.02'),
 Synset('crown.n.07'),
 Synset('limb.n.02'),
 Synset('stump.n.01'),
 Synset('trunk.n.01')]

wn.synset('tree.n.01').substance_meronyms()  # 樹的子元素

Out[80]: [Synset('heartwood.n.01'), Synset('sapwood.n.01')]


wn.synset('tree.n.01').member_holonyms()  # 組成樹的成員

Out[81]: [Synset('forest.n.01')]

for synset in wn.synsets('mint', wn.NOUN):  # 打印出同義詞之間具體的關係
    print(synset.name() + ':', synset.definition())
    
batch.n.02: (often followed by `of') a large number or amount or extent
mint.n.02: any north temperate plant of the genus Mentha with aromatic leaves and small mauve flowers
mint.n.03: any member of the mint family of plants
mint.n.04: the leaves of a mint plant used fresh or candied
mint.n.05: a candy that is flavored with a mint oil
mint.n.06: a plant where money is coined by authority of the government

wn.synset('mint.n.04').part_holonyms()

Out[84]: [Synset('mint.n.02')]


wn.synset('mint.n.04').substance_holonyms()

Out[85]: [Synset('mint.n.05')]


wn.synset('walk.v.01').entailments()

Out[86]: [Synset('step.v.01')]

wn.lemma('supply.n.02.supply').antonyms()  # 檢視反義詞

Out[87]: [Lemma('demand.n.02.demand')]

3、語義相似度

right = wn.synset('right_whale.n.01')
orca = wn.synset('orca.n.01')
minke = wn.synset('minke_whale.n.01')
tortoise = wn.synset('tortoise.n.01')
novel = wn.synset('novel.n.01')
right.lowest_common_hypernyms(minke)  # 查詢相關性比較強的詞語

Out[88]: [Synset('baleen_whale.n.01')]

wn.synset('baleen_whale.n.01').min_depth()

Out[89]: 14

right.path_similarity(minke)  # 比較相似度

Out[90]: 0.25