NLTKを使ったあれこれ（メモ）

# 環境構築

```
> pip install nltk
```

## WordNet
[WordNet Interface](https://www.nltk.org/howto/wordnet.html)

- 意味関係に焦点を当てた辞書
    - 15,287語, 117659の同義語集合
  日本語ワードネット（情報通信研究機構）: 約9万語
    - 上位語（Superordinate）と下位語（Hyponym）を定義

### ダウンロード

```python
import nltk
nltk.download("wordnet")
```

### 語義と同義語

```python
from nltk.corpus import wordnet as wn
print(wn.synsets("motorcar")) # 語義
print(wn.synset("car.n.01").lemma_names()) # 同義語
print(wn.synset("car.n.01").definition()) # 定義文
print(wn.synset("car.n.01").examples()) # 例文


"""
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[Synset('car.n.01')]
['car', 'auto', 'automobile', 'machine', 'motorcar']
a motor vehicle with four wheels; usually propelled by an internal combustion engine
['he needs a car to get to work']
"""
```

```python
print(wn.synset("car.n.01").lemmas()) # 見出し語集合
print(wn.lemma("car.n.01.automobile")) # 見出し
print(wn.lemma("car.n.01.automobile").synset()) # 語義
print(wn.lemma("car.n.01.automobile").name()) # 見出し語名

"""
[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')]
Lemma('car.n.01.automobile')
Synset('car.n.01')
automobile
"""
```

```python
print(wn.synsets("car")) # 複数の語義
for synset in wn.synsets("car"): # 語義ごとの同義語
  print(synset.lemma_names())
print(wn.lemmas("car")) # 見出し語集合

"""
[Synset('car.n.01'), Synset('car.n.02'), Synset('car.n.03'), Synset('car.n.04'), Synset('cable_car.n.01')]
['car', 'auto', 'automobile', 'machine', 'motorcar']
['car', 'railcar', 'railway_car', 'railroad_car']
['car', 'gondola']
['car', 'elevator_car']
['cable_car', 'car']
[Lemma('car.n.01.car'), Lemma('car.n.02.car'), Lemma('car.n.03.car'), Lemma('car.n.04.car'), Lemma('cable_car.n.01.car')]
"""
```

### 上位語と下位語

```python
motorcar = wn.synset("car.n.01") # 語義
print(motorcar.hypernyms()) # 上位語集合
print(motorcar.hyponyms()) # 下位語集合

"""
[Synset('motor_vehicle.n.01')]
[Synset('ambulance.n.01'), Synset('beach_wagon.n.01'), Synset('bus.n.04'), Synset('cab.n.03'), Synset('compact.n.03'), Synset('convertible.n.01'), Synset('coupe.n.01'), Synset('cruiser.n.01'), Synset('electric.n.01'), Synset('gas_guzzler.n.01'), Synset('hardtop.n.01'), Synset('hatchback.n.01'), Synset('horseless_carriage.n.01'), Synset('hot_rod.n.01'), Synset('jeep.n.01'), Synset('limousine.n.01'), Synset('loaner.n.02'), Synset('minicar.n.01'), Synset('minivan.n.01'), Synset('model_t.n.01'), Synset('pace_car.n.01'), Synset('racer.n.02'), Synset('roadster.n.01'), Synset('sedan.n.01'), Synset('sport_utility.n.01'), Synset('sports_car.n.01'), Synset('stanley_steamer.n.01'), Synset('stock_car.n.01'), Synset('subcompact.n.01'), Synset('touring_car.n.01'), Synset('used-car.n.01')]
"""
```

```python
paths = motorcar.hypernym_paths() # 最上位語への経路
print(len(paths)) # 経路は2つ
print([synset.name() for synset in paths[0]]) # 経路1
print([synset.name() for synset in paths[0]]) # 経路2
print(motorcar.root_hypernyms()) # 最上位語

"""
2
['entity.n.01', 'physical_entity.n.01', 'object.n.01', 'whole.n.02', 'artifact.n.01', 'instrumentality.n.03', 'container.n.01', 'wheeled_vehicle.n.01', 'self-propelled_vehicle.n.01', 'motor_vehicle.n.01', 'car.n.01']
['entity.n.01', 'physical_entity.n.01', 'object.n.01', 'whole.n.02', 'artifact.n.01', 'instrumentality.n.03', 'container.n.01', 'wheeled_vehicle.n.01', 'self-propelled_vehicle.n.01', 'motor_vehicle.n.01', 'car.n.01']
[Synset('entity.n.01')]
"""
```

### 全体と構成要素

```python
print(wn.synset("tree.n.01").part_meronyms()) # 部分
print(wn.synset("tree.n.01").substance_meronyms()) # 材料
print(wn.synset("tree.n.01").member_holonyms()) # 構成員

"""
[Synset('burl.n.02'), Synset('crown.n.07'), Synset('limb.n.02'), Synset('stump.n.01'), Synset('trunk.n.01')]
[Synset('heartwood.n.01'), Synset('sapwood.n.01')]
[Synset('forest.n.01')]
"""
```

### 含意と反意

```python
print(wn.synset("walk.v.01").entailments()) # 含意関係
print(wn.synset("eat.v.01").entailments()) # 含意関係
print(wn.lemma("supply.n.02.supply").antonyms()) # 反意語
print(wn.lemma("horizontal.a.01.horizontal").antonyms()) # 反意語

"""
[Synset('step.v.01')]
[Synset('chew.v.01'), Synset('swallow.v.01')]
[Lemma('demand.n.02.demand')]
[Lemma('inclined.a.02.inclined'), Lemma('vertical.a.01.vertical')]
"""
```

### 単語の類似度計算
- WordNetの上位語・下位語の考え方を利用
  - 距離を計算して近いほど類似しているとする
 
```python
nickel = wn.synset("nickel.n.02") # 語義の取得
dime = wn.synset("dime.n.01") # 語義の取得
money = wn.synset("money.n.01") # 語義の取得
print(nickel.lowest_common_hypernyms(dime)) # nickelとdimeの最短共通祖先
print(nickel.lowest_common_hypernyms(money)) # nickelとmoneyの最短共通祖先

"""
[Synset('coin.n.01')]
[Synset('medium_of_exchange.n.01')]
"""
```

```python
print(wn.synset("coin.n.01").min_depth()) # 最上位概念からの深さの計算
print(wn.synset("medium_of_exchange.n.01").min_depth()) # 最上位概念からの深さの計算
## 最上位概念からの深さが深いほど具体的で，浅ければ一般的らしい
print(nickel.path_similarity(dime)) # nickelとdimeの類似度
print(nickel.path_similarity(money)) # nickelとmoneyの類似度

"""
8
5
0.3333333333333333
0.16666666666666666
"""
```

## コーパスへのアクセス（全コーパス共通）

```python
import nltk
nltk.download("gutenberg")
nltk.download("punkt")
from nltk.corpus import gutenberg

# 生の文へのアクセス
raw = gutenberg.raw("burgess-busterbrown.txt")
print(raw[1:20])

# 単語へのアクセス
words = gutenberg.words("burgess-busterbrown.txt")
print(words[1:20])

# 文へのアクセス
sents = gutenberg.sents("burgess-busterbrown.txt")
print(sents[1:20])

"""
[nltk_data] Downloading package gutenberg to /root/nltk_data...
[nltk_data]   Package gutenberg is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
The Adventures of B
['The', 'Adventures', 'of', 'Buster', 'Bear', 'by', 'Thornton', 'W', '.', 'Burgess', '1920', ']', 'I', 'BUSTER', 'BEAR', 'GOES', 'FISHING', 'Buster', 'Bear']
[['I'], ['BUSTER', 'BEAR', 'GOES', 'FISHING'], ['Buster', 'Bear', 'yawned', 'as', 'he', 'lay', 'on', 'his', 'comfortable', 'bed', 'of', 'leaves', 'and', 'watched', 'the', 'first', 'early', 'morning', 'sunbeams', 'creeping', 'through', 'the', 'Green', 'Forest', 'to', 'chase', 'out', 'the', 'Black', 'Shadows', '.'], ['Once', 'more', 'he', 'yawned', ',', 'and', 'slowly', 'got', 'to', 'his', 'feet', 'and', 'shook', 'himself', '.'], ['Then', 'he', 'walked', 'over', 'to', 'a', 'big', 'pine', '-', 'tree', ',', 'stood', 'up', 'on', 'his', 'hind', 'legs', ',', 'reached', 'as', 'high', 'up', 'on', 'the', 'trunk', 'of', 'the', 'tree', 'as', 'he', 'could', ',', 'and', 'scratched', 'the', 'bark', 'with', 'his', 'great', 'claws', '.'], ['After', 'that', 'he', 'yawned', 'until', 'it', 'seemed', 'as', 'if', 'his', 'jaws', 'would', 'crack', ',', 'and', 'then', 'sat', 'down', 'to', 'think', 'what', 'he', 'wanted', 'for', 'breakfast', '.'], ['While', 'he', 'sat', 'there', ',', 'trying', 'to', 'make', 'up', 'his', 'mind', 'what', 'would', 'taste', 'best', ',', 'he', 'was', 'listening', 'to', 'the', 'sounds', 'that', 'told', 'of', 'the', 'waking', 'of', 'all', 'the', 'little', 'people', 'who', 'live', 'in', 'the', 'Green', 'Forest', '.'], ['He', 'heard', 'Sammy', 'Jay', 'way', 'off', 'in', 'the', 'distance', 'screaming', ',', '"', 'Thief', '!'], ['Thief', '!"'], ['and', 'grinned', '.'], ['"', 'I', 'wonder', ',"', 'thought', 'Buster', ',', '"', 'if', 'some', 'one', 'has', 'stolen', 'Sammy', "'", 's', 'breakfast', ',', 'or', 'if', 'he', 'has', 'stolen', 'the', 'breakfast', 'of', 'some', 'one', 'else', '.'], ['Probably', 'he', 'is', 'the', 'thief', 'himself', '."'], ['He', 'heard', 'Chatterer', 'the', 'Red', 'Squirrel', 'scolding', 'as', 'fast', 'as', 'he', 'could', 'make', 'his', 'tongue', 'go', 'and', 'working', 'himself', 'into', 'a', 'terrible', 'rage', '.'], ['"', 'Must', 'be', 'that', 'Chatterer', 'got', 'out', 'of', 'bed', 'the', 'wrong', 'way', 'this', 'morning', ',"', 'thought', 'he', '.'], ['He', 'heard', 'Blacky', 'the', 'Crow', 'cawing', 'at', 'the', 'top', 'of', 'his', 'lungs', ',', 'and', 'he', 'knew', 'by', 'the', 'sound', 'that', 'Blacky', 'was', 'getting', 'into', 'mischief', 'of', 'some', 'kind', '.'], ['He', 'heard', 'the', 'sweet', 'voices', 'of', 'happy', 'little', 'singers', ',', 'and', 'they', 'were', 'good', 'to', 'hear', '.'], ['But', 'most', 'of', 'all', 'he', 'listened', 'to', 'a', 'merry', ',', 'low', ',', 'silvery', 'laugh', 'that', 'never', 'stopped', 'but', 'went', 'on', 'and', 'on', ',', 'until', 'he', 'just', 'felt', 'as', 'if', 'he', 'must', 'laugh', 'too', '.'], ['It', 'was', 'the', 'voice', 'of', 'the', 'Laughing', 'Brook', '.'], ['And', 'as', 'Buster', 'listened', 'it', 'suddenly', 'came', 'to', 'him', 'just', 'what', 'he', 'wanted', 'for', 'breakfast', '.']]
"""
```

## グーテンベルクコーパス
- 42,000冊のフリーの電子書籍を収集

```python
import nltk
nltk.download("gutenberg")
nltk.download('punkt')　# gutenbergだけだとエラーが出たので追加
from nltk.corpus import gutenberg
print(gutenberg.fileids()) # タイトル一覧(なぜか少ない)
bible_words = gutenberg.words("bible-kjv.txt") # 聖書？
len(bible_words) # 総単語数

# 作品ごとに単語の平均長・文の平均長・各単語の出現回数の平均
print("単語の平均長　文の平均長　各単語のテキスト中の出現回数の平均")
for fileid in gutenberg.fileids():
  num_chars = len(gutenberg.raw(fileid))
  num_words = len(gutenberg.words(fileid))
  num_sents = len(gutenberg.sents(fileid))
  num_vocab = len(set([w.lower for w in gutenberg.words(fileid)]))
  print(round(num_chars/num_words),round(num_words/num_sents),round(num_words/num_vocab),fileid)

"""
[nltk_data] Downloading package gutenberg to /root/nltk_data...
[nltk_data]   Package gutenberg is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Unzipping tokenizers/punkt.zip.
['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']
単語の平均長　文の平均長　各単語のテキスト中の出現回数の平均
5 25 1 austen-emma.txt
5 26 1 austen-persuasion.txt
5 28 1 austen-sense.txt
4 34 1 bible-kjv.txt
5 19 1 blake-poems.txt
4 19 1 bryant-stories.txt
4 18 1 burgess-busterbrown.txt
4 20 1 carroll-alice.txt
5 20 1 chesterton-ball.txt
5 23 1 chesterton-brown.txt
5 18 1 chesterton-thursday.txt
4 21 1 edgeworth-parents.txt
5 26 1 melville-moby_dick.txt
5 52 1 milton-paradise.txt
4 12 1 shakespeare-caesar.txt
4 12 1 shakespeare-hamlet.txt
4 12 1 shakespeare-macbeth.txt
5 36 1 whitman-leaves.txt
"""
```

```python
# テキストを文に分割
macbeth_sentences = gutenberg.sents("shakespeare-macbeth.txt") # マクベスを文に分割して取得
print(macbeth_sentences) # 文の内容を確認できる
print(macbeth_sentences[1000]) # 特定の文も確認できる

"""
[['[', 'The', 'Tragedie', 'of', 'Macbeth', 'by', 'William', 'Shakespeare', '1603', ']'], ['Actus', 'Primus', '.'], ...]
['quite', 'vnmann', "'", 'd', 'in', 'folly']
"""
```

### チャットコーパス

```python
import nltk
nltk.download("nps_chat")
from nltk.corpus import nps_chat

chatroom = nps_chat.posts("10-19-20s_706posts.xml")
print(chatroom[190])

"""
[nltk_data] Downloading package nps_chat to /root/nltk_data...
[nltk_data]   Package nps_chat is already up-to-date!
['wow', '.', 'twice', ',', 'I', "'m", 'impressed', '.']
"""
```

### ブラウンコーパス
- 英語の大規模コーパス
- ブラウン州立大学
- ブラウン州立大学
- 5億8,300万語，293,181単語
- ジャンルごとに分類

```python
import nltk
nltk.download("brown")
from nltk.corpus import brown

print(brown.categories()) # 文書ジャンル
print(brown.words(categories="news")) # ジャンルを指定して単語区切りで出力
print(brown.sents(categories=["news", "editorial", "reviews"])) # ジャンルを指定して文区切りで出力

"""
[nltk_data] Downloading package brown to /root/nltk_data...
[nltk_data]   Package brown is already up-to-date!
['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']
['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
[['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', '.'], ...]
"""
```

```python
news_text = brown.words(categories="news") # newsジャンルを単語区切りで取得
fdist = nltk.FreqDist([w.lower() for w in news_text]) # ニュース中の単語の出現頻度分布
modals = ["can", "could", "may", "might", "must", "will"] # 助動詞のリスト
for m in modals: # 助動詞の頻度を調べる
  print(m + ":", fdist[m], end=" ")

"""
can: 94 could: 87 may: 93 might: 38 must: 53 will: 389
"""
```

```python
cfd = nltk.ConditionalFreqDist(
    (genre, word) for genre in brown.categories() for word in brown.words(categories=genre)
) # ジャンルと単語のタプルを作って頻度分布を計算する
genres = ["news", "religion", "hobbies", "science_fiction", "romance", "humor"] # ジャンルのリスト
print(cfd.tabulate(conditions = genres, samples = modals)) # ジャンルごとの単語の頻度を取得

"""
                  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 
None
"""
```

### ロイターコーパス
- ロイター通信社によるニュースコーパス

```python
import nltk
nltk.download("reuters")
from nltk.corpus import reuters
print(reuters.fileids()) # ファイルIDの一覧
print(reuters.categories()) # トピック
print(reuters.categories("training/9865")) # 文書のトピック
print(reuters.fileids(["barley", "corn"])) # トピックの文書

"""
[nltk_data] Downloading package reuters to /root/nltk_data...
[nltk_data]   Package reuters is already up-to-date!
['test/14826', 'test/14828', 'test/14829', 'test/14832', 'test/14833', 'test/14839', 'test/14840', 'test/14841', 'test/14842', 'test/14843', 'test/14844', 'test/14849', 'test/14852', 'test/14854', 'test/14858', 'test/14859', 'test/14860', 'test/14861', 'test/14862', 'test/14863', 'test/14865', 'test/14867', 'test/14872', 'test/14873', 'test/14875', 'test/14876', 'test/14877', 'test/14881', 'test/14882', 'test/14885', 'test/14886', 'test/14888', 'test/14890', 'test/14891', 'test/14892', 'test/14899', 'test/14900', 'test/14903', 'test/14904', 'test/14907', 'test/14909', 'test/14911', 'test/14912', 'test/14913', 'test/14918', 'test/14919', 'test/14921', 'test/14922', 'test/14923', 'test/14926', 'test/14928', 'test/14930', 'test/14931', 'test/14932', 'test/14933', 'test/14934', 'test/14941', 'test/14943', 'test/14949', 'test/14951', 'test/14954', 'test/14957', 'test/14958', 'test/14959', 'test/14960', 'test/14962', 'test/14963', 'test/14964', 'test/14965', 'test/14967', 'test/14968', 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['acq', 'alum', 'barley', 'bop', 'carcass', 'castor-oil', 'cocoa', 'coconut', 'coconut-oil', 'coffee', 'copper', 'copra-cake', 'corn', 'cotton', 'cotton-oil', 'cpi', 'cpu', 'crude', 'dfl', 'dlr', 'dmk', 'earn', 'fuel', 'gas', 'gnp', 'gold', 'grain', 'groundnut', 'groundnut-oil', 'heat', 'hog', 'housing', 'income', 'instal-debt', 'interest', 'ipi', 'iron-steel', 'jet', 'jobs', 'l-cattle', 'lead', 'lei', 'lin-oil', 'livestock', 'lumber', 'meal-feed', 'money-fx', 'money-supply', 'naphtha', 'nat-gas', 'nickel', 'nkr', 'nzdlr', 'oat', 'oilseed', 'orange', 'palladium', 'palm-oil', 'palmkernel', 'pet-chem', 'platinum', 'potato', 'propane', 'rand', 'rape-oil', 'rapeseed', 'reserves', 'retail', 'rice', 'rubber', 'rye', 'ship', 'silver', 'sorghum', 'soy-meal', 'soy-oil', 'soybean', 'strategic-metal', 'sugar', 'sun-meal', 'sun-oil', 'sunseed', 'tea', 'tin', 'trade', 'veg-oil', 'wheat', 'wpi', 'yen', 'zinc']
['barley', 'corn', 'grain', 'wheat']
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"""
```

```
print(reuters.words("training/9865")[:14]) # 文書の先頭14単語を取得
print(reuters.words(["training/9865", "training/9880"])) # 複数の文書を単語区切りで取得
print(reuters.words(categories = "barley")) # トピックを指定して単語区切りで取得
print(reuters.words(categories = ["barley", "corn"])) # 複数のトピックを指定して単語区切りで取得

"""
['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', 'BIDS', 'DETAILED', 'French', 'operators', 'have', 'requested', 'licences', 'to', 'export']
['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', ...]
['FRENCH', 'FREE', 'MARKET', 'CEREAL', 'EXPORT', ...]
['THAI', 'TRADE', 'DEFICIT', 'WIDENS', 'IN', 'FIRST', ...]
"""
```

### 大統領就任演説コーパス
- アメリカ大統領による就任演説集

```python
import nltk
nltk.download("inaugural")
from nltk.corpus import inaugural
print(inaugural.fileids()) # ファイルID一覧
print([fileid[:4] for fileid in inaugural.fileids()]) # 就任した年を取得

"""
[nltk_data] Downloading package inaugural to /root/nltk_data...
[nltk_data]   Package inaugural is already up-to-date!
['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', '1801-Jefferson.txt', '1805-Jefferson.txt', '1809-Madison.txt', '1813-Madison.txt', '1817-Monroe.txt', '1821-Monroe.txt', '1825-Adams.txt', '1829-Jackson.txt', '1833-Jackson.txt', '1837-VanBuren.txt', '1841-Harrison.txt', '1845-Polk.txt', '1849-Taylor.txt', '1853-Pierce.txt', '1857-Buchanan.txt', '1861-Lincoln.txt', '1865-Lincoln.txt', '1869-Grant.txt', '1873-Grant.txt', '1877-Hayes.txt', '1881-Garfield.txt', '1885-Cleveland.txt', '1889-Harrison.txt', '1893-Cleveland.txt', '1897-McKinley.txt', '1901-McKinley.txt', '1905-Roosevelt.txt', '1909-Taft.txt', '1913-Wilson.txt', '1917-Wilson.txt', '1921-Harding.txt', '1925-Coolidge.txt', '1929-Hoover.txt', '1933-Roosevelt.txt', '1937-Roosevelt.txt', '1941-Roosevelt.txt', '1945-Roosevelt.txt', '1949-Truman.txt', '1953-Eisenhower.txt', '1957-Eisenhower.txt', '1961-Kennedy.txt', '1965-Johnson.txt', '1969-Nixon.txt', '1973-Nixon.txt', '1977-Carter.txt', '1981-Reagan.txt', '1985-Reagan.txt', '1989-Bush.txt', '1993-Clinton.txt', '1997-Clinton.txt', '2001-Bush.txt', '2005-Bush.txt', '2009-Obama.txt', '2013-Obama.txt', '2017-Trump.txt']
['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', '2013', '2017']
"""
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