How To Make A Word Bank In Python

In this tutorial, we will explore how to build a word bank using Python.

A word bank is a tool that can be used to store and manage a collection of words, mainly for the purpose of vocabulary building and learning. It can be designed based on various criteria, such as word frequency, similarity, or other customized requirements.

By building a word bank using Python, you can easily write code to perform word analysis, word extraction, and word management tasks.

Step 1: Import necessary libraries

First, you need to import the necessary libraries. For this tutorial, you will need the nltk library. If you don’t have this library already installed, you can install it using pip with the following command:

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pip install nltk

Once you have the nltk library, import it in your python script:

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import nltk

In addition, you might want to download the punkt, stopwords, and wordnet datasets using the command below:

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nltk.download("punkt")
nltk.download("stopwords")
nltk.download("wordnet")

Step 2: Define the text to analyze

For this tutorial, let’s use the following text as an example. You can replace it with your own text as needed:

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sample_text = """A word bank is a tool that can be used to store and manage a collection of words, mainly for the purpose of vocabulary building and learning. It can be designed based on various criteria, such as word frequency, similarity, or other customized requirements."""

Step 3: Tokenize the text

Tokenization is the process of splitting a large paragraph into words or segments, using a process called lexical analysis. With the help of the nltk library, you can tokenize the text into words, as shown below:

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from nltk.tokenize import word_tokenize
 
words = word_tokenize(sample_text)
print(words)

Step 4: Remove stopwords and punctuation

Stop words are common words that do not carry much meaning and thus are often removed from the text when processing it. Punctuation marks also need to be removed. To do this, use the following code:

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from nltk.corpus import stopwords
from string import punctuation
 
stop_words = set(stopwords.words("english"))
filtered_words = [word for word in words if word.lower() not in stop_words and word not in punctuation]
print(filtered_words)

Step 5: Lemmatize the words

Lemmatization is the process of reducing words to their base form. This is helpful when creating a word bank because it combines words with similar meanings into a single entry. To perform lemmatization, use the following code:

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from nltk.stem import WordNetLemmatizer
 
lemmatizer = WordNetLemmatizer()
lemmatized_words = [lemmatizer.lemmatize(word) for word in filtered_words]
print(lemmatized_words)

Step 6: Create the word bank

Finally, you can create your word bank using the processed words:

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word_bank = set(lemmatized_words)
print(word_bank)

Full code

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import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from string import punctuation
from nltk.stem import WordNetLemmatizer
 
nltk.download("punkt")
nltk.download("stopwords")
nltk.download("wordnet")
 
sample_text = """A word bank is a tool that can be used to store and manage a collection of words, mainly for the purpose of vocabulary building and learning. It can be designed based on various criteria, such as word frequency, similarity, or other customized requirements."""
 
words = word_tokenize(sample_text)
 
stop_words = set(stopwords.words("english"))
filtered_words = [word for word in words if word.lower() not in stop_words and word not in punctuation]
 
lemmatizer = WordNetLemmatizer()
lemmatized_words = [lemmatizer.lemmatize(word) for word in filtered_words]
 
word_bank = set(lemmatized_words)
print(word_bank)

Expected Output

{'similarity', 'vocabulary', 'word', 'used', 'learning', 'manage', 'store', 'frequency', 'building', 'bank', 'criteria', 'designed', 'mainly', 'tool', 'customize', 'collection', 'requirement', 'purpose', 'based', 'variou'}

Conclusion

Now, you know how to create a word bank in Python that contains unique, meaningful words after tokenizing, removing stop words and punctuation, and lemmatizing the text. This word bank can further be used for various language processing tasks and analysis.