import random
import nltk

# Download the Brown corpus if not already downloaded
nltk.download('brown')

def generate_sentence():
    # Load the Brown corpus and extract a random word
    words = nltk.corpus.brown.words()
    word = random.choice(words)
    
    # Determine the word's part of speech
    pos = nltk.pos_tag([word])[0][1]
    if pos.startswith('N'):
        noun = word
        verb = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('V')])
        adjective = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('J')])
    elif pos.startswith('V'):
        verb = word
        noun = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('N')])
        adjective = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('J')])
    elif pos.startswith('J'):
        adjective = word
        noun = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('N')])
        verb = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('V')])
    
    # Construct the sentence
    adverb = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('RB')])
    preposition = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('IN')])
    article = random.choice(['a', 'an', 'the'])
    object_noun = random.choice([w for w, t in nltk.corpus.brown.tagged_words() if t.startswith('N')])
    
    sentence = f"{article.capitalize()} {adjective} {noun} {verb} {adverb} {preposition} {article} {object_noun}."
    return sentence

if __name__ == '__main__':
    for i in range(10):
        print(generate_sentence())

{}
region:us
TAGS
#region-us 

import random
import nltk

# Download the Brown corpus if not already downloaded
nltk.download('brown')

def generate_sentence():
    # Load the Brown corpus and extract a random word
    words = URL()
    word = URL(words)
    
    # Determine the word's part of speech
    pos = nltk.pos_tag([word])[0][1]
    if pos.startswith('N'):
        noun = word
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('V'):
        verb = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('J'):
        adjective = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
    
    # Construct the sentence
    adverb = URL([w for w, t in URL.tagged_words() if t.startswith('RB')])
    preposition = URL([w for w, t in URL.tagged_words() if t.startswith('IN')])
    article = URL(['a', 'an', 'the'])
    object_noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
    
    sentence = f"{article.capitalize()} {adjective} {noun} {verb} {adverb} {preposition} {article} {object_noun}."
    return sentence

if __name__ == '__main__':
    for i in range(10):
        print(generate_sentence())

# Download the Brown corpus if not already downloaded
nltk.download('brown')

def generate_sentence():
    # Load the Brown corpus and extract a random word
    words = URL()
    word = URL(words)
    
    # Determine the word's part of speech
    pos = nltk.pos_tag([word])[0][1]
    if pos.startswith('N'):
        noun = word
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('V'):
        verb = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('J'):
        adjective = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
    
    # Construct the sentence
    adverb = URL([w for w, t in URL.tagged_words() if t.startswith('RB')])
    preposition = URL([w for w, t in URL.tagged_words() if t.startswith('IN')])
    article = URL(['a', 'an', 'the'])
    object_noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
    
    sentence = f"{article.capitalize()} {adjective} {noun} {verb} {adverb} {preposition} {article} {object_noun}."
    return sentence

if __name__ == '__main__':
    for i in range(10):
        print(generate_sentence())
TAGS
#region-us 
 # Download the Brown corpus if not already downloaded
nltk.download('brown')

def generate_sentence():
    # Load the Brown corpus and extract a random word
    words = URL()
    word = URL(words)
    
    # Determine the word's part of speech
    pos = nltk.pos_tag([word])[0][1]
    if pos.startswith('N'):
        noun = word
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('V'):
        verb = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('J'):
        adjective = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
    
    # Construct the sentence
    adverb = URL([w for w, t in URL.tagged_words() if t.startswith('RB')])
    preposition = URL([w for w, t in URL.tagged_words() if t.startswith('IN')])
    article = URL(['a', 'an', 'the'])
    object_noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
    
    sentence = f"{article.capitalize()} {adjective} {noun} {verb} {adverb} {preposition} {article} {object_noun}."
    return sentence

if __name__ == '__main__':
    for i in range(10):
        print(generate_sentence())
TAGS
#region-us 
# Download the Brown corpus if not already downloaded
nltk.download('brown')

def generate_sentence():
    # Load the Brown corpus and extract a random word
    words = URL()
    word = URL(words)
    
    # Determine the word's part of speech
    pos = nltk.pos_tag([word])[0][1]
    if pos.startswith('N'):
        noun = word
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('V'):
        verb = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        adjective = URL([w for w, t in URL.tagged_words() if t.startswith('J')])
    elif pos.startswith('J'):
        adjective = word
        noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
        verb = URL([w for w, t in URL.tagged_words() if t.startswith('V')])
    
    # Construct the sentence
    adverb = URL([w for w, t in URL.tagged_words() if t.startswith('RB')])
    preposition = URL([w for w, t in URL.tagged_words() if t.startswith('IN')])
    article = URL(['a', 'an', 'the'])
    object_noun = URL([w for w, t in URL.tagged_words() if t.startswith('N')])
    
    sentence = f"{article.capitalize()} {adjective} {noun} {verb} {adverb} {preposition} {article} {object_noun}."
    return sentence

if __name__ == '__main__':
    for i in range(10):
        print(generate_sentence())