> pip install nltk
WordNet WordNet Interface
import nltk
nltk.download("wordnet")
from nltk.corpus import wordnet as wn
print(wn.synsets("motorcar")) #Bedeutung
print(wn.synset("car.n.01").lemma_names()) #Synonym
print(wn.synset("car.n.01").definition()) #Definitionsanweisung
print(wn.synset("car.n.01").examples()) #Beispielsatz
"""
[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()) #Stichwort gesetzt
print(wn.lemma("car.n.01.automobile")) #Überschrift
print(wn.lemma("car.n.01.automobile").synset()) #Bedeutung
print(wn.lemma("car.n.01.automobile").name()) #Stichwortname
"""
[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")) #Mehrfachbedeutungen
for synset in wn.synsets("car"): #Synonyme für jede Bedeutung
print(synset.lemma_names())
print(wn.lemmas("car")) #Stichwort gesetzt
"""
[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')]
"""
motorcar = wn.synset("car.n.01") #Bedeutung
print(motorcar.hypernyms()) #High-Level-Wortsatz
print(motorcar.hyponyms()) #Unterwortsatz
"""
[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 zum obersten Wort
print(len(paths)) #Es gibt zwei Routen
print([synset.name() for synset in paths[0]]) #Route 1
print([synset.name() for synset in paths[0]]) #Route 2
print(motorcar.root_hypernyms()) #Top Wort
"""
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')]
"""
print(wn.synset("tree.n.01").part_meronyms()) #Teil
print(wn.synset("tree.n.01").substance_meronyms()) #Material
print(wn.synset("tree.n.01").member_holonyms()) #Mitglied
"""
[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')]
"""
print(wn.synset("walk.v.01").entailments()) #Implikationen
print(wn.synset("eat.v.01").entailments()) #Implikationen
print(wn.lemma("supply.n.02.supply").antonyms()) #Anti-Worte
print(wn.lemma("horizontal.a.01.horizontal").antonyms()) #Anti-Worte
"""
[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')]
"""
--Verwenden Sie die Idee der oberen und unteren Wörter von WordNet --Berechnen Sie die Entfernung und nehmen Sie an, dass sie sich umso ähnlicher sind, je näher sie sind.
nickel = wn.synset("nickel.n.02") #Bedeutungserwerb
dime = wn.synset("dime.n.01") #Bedeutungserwerb
money = wn.synset("money.n.01") #Bedeutungserwerb
print(nickel.lowest_common_hypernyms(dime)) #Kürzester gemeinsamer Vorfahr von Nickel und Cent
print(nickel.lowest_common_hypernyms(money)) #Der kürzeste gemeinsame Vorfahr von Nickel und Geld
"""
[Synset('coin.n.01')]
[Synset('medium_of_exchange.n.01')]
"""
print(wn.synset("coin.n.01").min_depth()) #Tiefenberechnung aus dem Top-Level-Konzept
print(wn.synset("medium_of_exchange.n.01").min_depth()) #Tiefenberechnung aus dem Top-Level-Konzept
##Je tiefer die Tiefe des Top-Konzepts ist, desto konkreter ist es und je flacher es ist, desto allgemeiner erscheint es.
print(nickel.path_similarity(dime)) #Ähnlichkeit zwischen Nickel und Cent
print(nickel.path_similarity(money)) #Ähnlichkeit zwischen Nickel und Geld
"""
8
5
0.3333333333333333
0.16666666666666666
"""
import nltk
nltk.download("gutenberg")
nltk.download("punkt")
from nltk.corpus import gutenberg
#Zugriff auf Rohtext
raw = gutenberg.raw("burgess-busterbrown.txt")
print(raw[1:20])
#Zugang zu Wörtern
words = gutenberg.words("burgess-busterbrown.txt")
print(words[1:20])
#Zugriff auf die Anweisung
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', '.']]
"""
import nltk
nltk.download("gutenberg")
nltk.download('punkt') #Ich habe einen Fehler bekommen, wenn es nur gutenberg war, also habe ich ihn hinzugefügt
from nltk.corpus import gutenberg
print(gutenberg.fileids()) #Titelliste(Irgendwie wenige)
bible_words = gutenberg.words("bible-kjv.txt") #Bibel?
len(bible_words) #Gesamtzahl der Wörter
#Durchschnittliche Länge von Wörtern, durchschnittliche Länge von Sätzen, durchschnittliche Anzahl von Vorkommen jedes Wortes für jede Arbeit
print("Durchschnittliche Länge von Wörtern Durchschnittliche Länge von Sätzen Durchschnittliche Anzahl von Vorkommen jedes Wortes im 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']
Durchschnittliche Länge von Wörtern Durchschnittliche Länge von Sätzen Durchschnittliche Anzahl von Vorkommen jedes Wortes im 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
"""
#Teilen Sie Text in Sätze
macbeth_sentences = gutenberg.sents("shakespeare-macbeth.txt") #Holen Sie sich Macbeth, indem Sie es in Sätze teilen
print(macbeth_sentences) #Sie können den Inhalt des Satzes überprüfen
print(macbeth_sentences[1000]) #Sie können auch bestimmte Sätze überprüfen
"""
[['[', 'The', 'Tragedie', 'of', 'Macbeth', 'by', 'William', 'Shakespeare', '1603', ']'], ['Actus', 'Primus', '.'], ...]
['quite', 'vnmann', "'", 'd', 'in', 'folly']
"""
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', '.']
"""
import nltk
nltk.download("brown")
from nltk.corpus import brown
print(brown.categories()) #Dokumentgenre
print(brown.words(categories="news")) #Geben Sie das Genre und die Ausgabe wortgetrennt an
print(brown.sents(categories=["news", "editorial", "reviews"])) #Geben Sie das Genre an und geben Sie es mit einem Satztrennzeichen aus
"""
[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") #Holen Sie sich das Nachrichtengenre durch Wörter getrennt
fdist = nltk.FreqDist([w.lower() for w in news_text]) #Häufigkeitsverteilung von Wörtern in Nachrichten
modals = ["can", "could", "may", "might", "must", "will"] #Liste der Hilfsverben
for m in modals: #Finden Sie die Häufigkeit von Hilfsverben heraus
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)
) #Erstellen Sie ein Genre- und Worttupel, um die Häufigkeitsverteilung zu berechnen
genres = ["news", "religion", "hobbies", "science_fiction", "romance", "humor"] #Liste der Genres
print(cfd.tabulate(conditions = genres, samples = modals)) #Holen Sie sich die Worthäufigkeit nach 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
"""
--News Korpus von Reuters
import nltk
nltk.download("reuters")
from nltk.corpus import reuters
print(reuters.fileids()) #Liste der Datei-IDs
print(reuters.categories()) #Thema
print(reuters.categories("training/9865")) #Dokumentthema
print(reuters.fileids(["barley", "corn"])) #Themendokument
"""
[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']
['test/14832', 'test/14858', 'test/15033', 'test/15043', 'test/15106', 'test/15287', 'test/15341', 'test/15618', 'test/15648', 'test/15649', 'test/15676', 'test/15686', 'test/15720', 'test/15728', 'test/15845', 'test/15856', 'test/15860', 'test/15863', 'test/15871', 'test/15875', 'test/15877', 'test/15890', 'test/15904', 'test/15906', 'test/15910', 'test/15911', 'test/15917', 'test/15952', 'test/15999', 'test/16012', 'test/16071', 'test/16099', 'test/16147', 'test/16525', 'test/16624', 'test/16751', 'test/16765', 'test/17503', 'test/17509', 'test/17722', 'test/17767', 'test/17769', 'test/18024', 'test/18035', 'test/18263', 'test/18482', 'test/18614', 'test/18908', 'test/18954', 'test/18973', 'test/19165', 'test/19275', 'test/19668', 'test/19721', 'test/19821', 'test/20018', 'test/20366', 'test/20637', 'test/20645', 'test/20649', 'test/20723', 'test/20763', 'test/21091', 'test/21243', 'test/21493', 'training/10120', 'training/10139', 'training/10172', 'training/10175', 'training/10319', 'training/10339', 'training/10487', 'training/10489', 'training/10519', 'training/1067', 'training/10701', 'training/10882', 'training/10956', 'training/11012', 'training/11085', 'training/11091', 'training/11208', 'training/11269', 'training/1131', 'training/11316', 'training/11392', 'training/11436', 'training/11607', 'training/11612', 'training/11729', 'training/11739', 'training/11769', 'training/11885', 'training/11936', 'training/11939', 'training/11964', 'training/12002', 'training/12052', 'training/12055', 'training/1215', 'training/12160', 'training/12311', 'training/12323', 'training/12372', 'training/12417', 'training/12428', 'training/12436', 'training/12500', 'training/12583', 'training/12587', 'training/1268', 'training/1273', 'training/12872', 'training/13099', 'training/13173', 'training/13179', 'training/1369', 'training/13744', 'training/13795', 'training/1385', 'training/13852', 'training/13856', 'training/1395', 'training/1399', 'training/14483', 'training/1582', 'training/1652', 'training/1777', 'training/1843', 'training/193', 'training/1952', 'training/197', 'training/1970', 'training/2044', 'training/2171', 'training/2172', 'training/2183', 'training/2191', 'training/2217', 'training/2232', 'training/2264', 'training/235', 'training/2382', 'training/2436', 'training/2456', 'training/2595', 'training/2599', 'training/2617', 'training/2727', 'training/2741', 'training/2749', 'training/2777', 'training/2848', 'training/2913', 'training/2922', 'training/2947', 'training/3132', 'training/3138', 'training/3191', 'training/327', 'training/3282', 'training/3299', 'training/3306', 'training/3324', 'training/3330', 'training/3337', 'training/3358', 'training/3401', 'training/3429', 'training/3847', 'training/3855', 'training/3881', 'training/3949', 'training/395', 'training/3979', 'training/3981', 'training/4047', 'training/4133', 'training/4280', 'training/4289', 'training/4296', 'training/4382', 'training/4490', 'training/4599', 'training/4825', 'training/4905', 'training/4939', 'training/4988', 'training/5', 'training/5003', 'training/501', 'training/5017', 'training/5033', 'training/5109', 'training/516', 'training/5185', 'training/5338', 'training/5467', 'training/5518', 'training/5531', 'training/5606', 'training/5610', 'training/5636', 'training/5637', 'training/5640', 'training/57', 'training/5847', 'training/5933', 'training/6', 'training/6142', 'training/6221', 'training/6236', 'training/6239', 'training/6259', 'training/6269', 'training/6386', 'training/6585', 'training/6588', 'training/6626', 'training/6735', 'training/6890', 'training/6897', 'training/694', 'training/7062', 'training/7205', 'training/7215', 'training/7336', 'training/7387', 'training/7389', 'training/7390', 'training/7395', 'training/7579', 'training/7700', 'training/7792', 'training/7917', 'training/7934', 'training/7943', 'training/8004', 'training/8140', 'training/8161', 'training/8166', 'training/8213', 'training/8257', 'training/8273', 'training/8400', 'training/8443', 'training/8446', 'training/8535', 'training/855', 'training/8759', 'training/8941', 'training/8983', 'training/8993', 'training/9058', 'training/9093', 'training/9094', 'training/934', 'training/9470', 'training/9521', 'training/9667', 'training/97', 'training/9865', 'training/9958', 'training/9989']
"""
print(reuters.words("training/9865")[:14]) #Holen Sie sich die ersten 14 Wörter eines Dokuments
print(reuters.words(["training/9865", "training/9880"])) #Holen Sie sich mehrere Dokumente durch Wörter getrennt
print(reuters.words(categories = "barley")) #Holen Sie sich durch Wortbruch durch Angabe eines Themas
print(reuters.words(categories = ["barley", "corn"])) #Trennen Sie sich von Wörtern, indem Sie mehrere Themen angeben
"""
['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', ...]
"""
import nltk
nltk.download("inaugural")
from nltk.corpus import inaugural
print(inaugural.fileids()) #Datei-ID-Liste
print([fileid[:4] for fileid in inaugural.fileids()]) #Holen Sie sich das Jahr, in dem Sie Ihr Amt angetreten haben
"""
[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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