[PYTHON] This and that using NLTK (memo)

Environment

> pip install nltk

WordNet WordNet Interface

--Dictionary focusing on semantic relationships --A set of synonyms for 15,287 words and 117659 Japanese WordNet (National Institute of Information and Communications Technology): Approximately 90,000 words --Define hypernyms (Superordinate) and hyponyms (Hyponym)

download

import nltk
nltk.download("wordnet")

Semantics and synonyms

from nltk.corpus import wordnet as wn
print(wn.synsets("motorcar")) #Semantics
print(wn.synset("car.n.01").lemma_names()) #synonym
print(wn.synset("car.n.01").definition()) #Definition statement
print(wn.synset("car.n.01").examples()) #Example sentence


"""
[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']
"""

print(wn.synset("car.n.01").lemmas()) #Headword set
print(wn.lemma("car.n.01.automobile")) #Heading
print(wn.lemma("car.n.01.automobile").synset()) #Semantics
print(wn.lemma("car.n.01.automobile").name()) #Heading word name

"""
[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
"""

print(wn.synsets("car")) #Multiple semantics
for synset in wn.synsets("car"): #Synonyms for each semantics
  print(synset.lemma_names())
print(wn.lemmas("car")) #Headword set

"""
[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')]
"""

Hypernym and hyponym

motorcar = wn.synset("car.n.01") #Semantics
print(motorcar.hypernyms()) #Hypernym set
print(motorcar.hyponyms()) #Hyponymy and hyperactivity

"""
[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')]
"""

paths = motorcar.hypernym_paths() #Route to hypernym
print(len(paths)) #There are two routes
print([synset.name() for synset in paths[0]]) #Route 1
print([synset.name() for synset in paths[0]]) #Route 2
print(motorcar.root_hypernyms()) #Hypernym

"""
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')]
"""

Whole and components

print(wn.synset("tree.n.01").part_meronyms()) #part
print(wn.synset("tree.n.01").substance_meronyms()) #material
print(wn.synset("tree.n.01").member_holonyms()) #member

"""
[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')]
"""

Implications and rebellions

print(wn.synset("walk.v.01").entailments()) #Implications
print(wn.synset("eat.v.01").entailments()) #Implications
print(wn.lemma("supply.n.02.supply").antonyms()) #Anti-words
print(wn.lemma("horizontal.a.01.horizontal").antonyms()) #Anti-words

"""
[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')]
"""

Word similarity calculation

--Using WordNet's concept of hypernyms and hyponyms --Calculate the distance and assume that the closer they are, the more similar they are.

nickel = wn.synset("nickel.n.02") #Acquisition of semantics
dime = wn.synset("dime.n.01") #Acquisition of semantics
money = wn.synset("money.n.01") #Acquisition of semantics
print(nickel.lowest_common_hypernyms(dime)) #Shortest common ancestor of nickel and dime
print(nickel.lowest_common_hypernyms(money)) #The shortest common ancestor of nickel and money

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

print(wn.synset("coin.n.01").min_depth()) #Calculation of depth from top-level concept
print(wn.synset("medium_of_exchange.n.01").min_depth()) #Calculation of depth from top-level concept
##The deeper the depth from the top concept, the more specific it is, and the shallower it is, the more general it seems.
print(nickel.path_similarity(dime)) #Similarity between nickel and dime
print(nickel.path_similarity(money)) #Similarity between nickel and money

"""
8
5
0.3333333333333333
0.16666666666666666
"""

Access to the corpus (common to all corpora)

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

#Access to raw sentences
raw = gutenberg.raw("burgess-busterbrown.txt")
print(raw[1:20])

#Access to words
words = gutenberg.words("burgess-busterbrown.txt")
print(words[1:20])

#Access to sentences
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', '.']]
"""

Gutenberg Corpus

--Collect 42,000 free e-books

import nltk
nltk.download("gutenberg")
nltk.download('punkt')　#I got an error if it was only gutenberg, so I added it
from nltk.corpus import gutenberg
print(gutenberg.fileids()) #Title list(Somehow few)
bible_words = gutenberg.words("bible-kjv.txt") #Bible?
len(bible_words) #Total number of words

#Average length of words, average length of sentences, average number of occurrences of each word for each work
print("Average length of words Average length of sentences Average number of occurrences of each word in the text")
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']
Average length of words Average length of sentences Average number of occurrences of each word in the text
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
"""

#Split text into sentences
macbeth_sentences = gutenberg.sents("shakespeare-macbeth.txt") #Get Macbeth by dividing it into sentences
print(macbeth_sentences) #You can check the content of the sentence
print(macbeth_sentences[1000]) #You can also check specific sentences

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

Chat corpus

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', '.']
"""

Brown Corpus

--Large English corpus --Brown State University --Brown State University --583 million words, 293,181 words --Classified by genre

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

print(brown.categories()) #Document genre
print(brown.words(categories="news")) #Specify the genre and output word-separated
print(brown.sents(categories=["news", "editorial", "reviews"])) #Specify the genre and output with sentence delimiter

"""
[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', '.'], ...]
"""

news_text = brown.words(categories="news") #Get news genre word separated
fdist = nltk.FreqDist([w.lower() for w in news_text]) #Frequency distribution of words in news
modals = ["can", "could", "may", "might", "must", "will"] #List of auxiliary verbs
for m in modals: #Find out the frequency of auxiliary verbs
  print(m + ":", fdist[m], end=" ")

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

cfd = nltk.ConditionalFreqDist(
    (genre, word) for genre in brown.categories() for word in brown.words(categories=genre)
) #Calculate frequency distribution by creating tuples of genres and words
genres = ["news", "religion", "hobbies", "science_fiction", "romance", "humor"] #List of genres
print(cfd.tabulate(conditions = genres, samples = modals)) #Get word frequency by genre

"""
                  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
"""

Reuters Corpus

--News Corpus by Reuters

import nltk
nltk.download("reuters")
from nltk.corpus import reuters
print(reuters.fileids()) #List of file IDs
print(reuters.categories()) #topic
print(reuters.categories("training/9865")) #Document topic
print(reuters.fileids(["barley", "corn"])) #Topic document

"""
[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', 'test/14969', 'test/14970', 'test/14971', 'test/14974', 'test/14975', 'test/14978', 'test/14981', 'test/14982', 'test/14983', 'test/14984', 'test/14985', 'test/14986', 'test/14987', 'test/14988', 'test/14993', 'test/14995', 'test/14998', 'test/15000', 'test/15001', 'test/15002', 'test/15004', 'test/15005', 'test/15006', 'test/15011', 'test/15012', 'test/15013', 'test/15016', 'test/15017', 'test/15020', 'test/15023', 'test/15024', 'test/15026', 'test/15027', 'test/15028', 'test/15029', 'test/15031', 'test/15032', 'test/15033', 'test/15037', 'test/15038', 'test/15043', 'test/15045', 'test/15046', 'test/15048', 'test/15049', 'test/15052', 'test/15053', 'test/15055', 'test/15056', 'test/15060', 'test/15061', 'test/15062', 'test/15063', 'test/15065', 'test/15067', 'test/15069', 'test/15070', 'test/15074', 'test/15077', 'test/15078', 'test/15079', 'test/15082', 'test/15090', 'test/15091', 'test/15092', 'test/15093', 'test/15094', 'test/15095', 'test/15096', 'test/15097', 'test/15103', 'test/15104', 'test/15106', 'test/15107', 'test/15109', 'test/15110', 'test/15111', 'test/15112', 'test/15118', 'test/15119', 'test/15120', 'test/15121', 'test/15122', 'test/15124', 'test/15126', 'test/15128', 'test/15129', 'test/15130', 'test/15132', 'test/15136', 'test/15138', 'test/15141', 'test/15144', 'test/15145', 'test/15146', 'test/15149', 'test/15152', 'test/15153', 'test/15154', 'test/15156', 'test/15157', 'test/15161', 'test/15162', 'test/15171', 'test/15172', 'test/15175', 'test/15179', 'test/15180', 'test/15185', 'test/15188', 'test/15189', 'test/15190', 'test/15193', 'test/15194', 'test/15197', 'test/15198', 'test/15200', 'test/15204', 'test/15205', 'test/15206', 'test/15207', 'test/15208', 'test/15210', 'test/15211', 'test/15212', 'test/15213', 'test/15217', 'test/15219', 'test/15220', 'test/15221', 'test/15222', 'test/15223', 'test/15226', 'test/15227', 'test/15230', 'test/15233', 'test/15234', 'test/15237', 'test/15238', 'test/15239', 'test/15240', 'test/15242', 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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]) #Get the first 14 words of the document
print(reuters.words(["training/9865", "training/9880"])) #Get multiple documents separated by words
print(reuters.words(categories = "barley")) #Get by word break by specifying a topic
print(reuters.words(categories = ["barley", "corn"])) #Get word-separated by specifying multiple topics

"""
['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', ...]
"""

Presidential Inauguration Speech Corpus

--Collection of inaugural speeches by the President of the United States

import nltk
nltk.download("inaugural")
from nltk.corpus import inaugural
print(inaugural.fileids()) #File ID list
print([fileid[:4] for fileid in inaugural.fileids()]) #Get the year you took office

"""
[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']
"""