Use of named entity recognition information extraction technique.

Program:

import nltk
import string

# Download necessary NLTK resources
nltk.download('punkt')
nltk.download("stopwords")

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

# Define the quote
word_quote = "Have you a pen I can borrow?” she asked."

# Tokenize the quote into words
words_in_quote = word_tokenize(word_quote)

# Get the set of stop words in English
stop_words = set(stopwords.words("english"))

# Define a set of punctuation characters, including additional punctuation marks
additional_punctuation = "”"
punctuation = set(string.punctuation).union(set(additional_punctuation))

# Filter out the stop words and punctuation
filtered_list = [word for word in words_in_quote if word.casefold() not in stop_words and word not in punctuation and word not in string.punctuation]

# Print the filtered list of words
print(filtered_list)