Building a Bag of Words Model from Scratch: A Step Toward Lexical Representations

Introduction

Text data is inherently unstructured, and in Natural Language Processing (NLP), transforming raw text into a structured format is crucial. One of the fundamental techniques for achieving this transformation is the Bag of Words (BoW) model. Despite its simplicity, BoW forms the foundation for more advanced text representation methods such as TF-IDF and word embeddings.

This article documents an implementation of BoW from scratch, explaining its core principles, challenges, and future enhancements.

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Understanding the Bag of Words Model

The Bag of Words model represents text as a collection of words, disregarding grammar and word order but preserving word frequency. Each document is transformed into a vector where each dimension corresponds to a unique word in the vocabulary, and the value represents the word’s occurrence in the document.

For example, consider the following two sentences:

1.  t1 = "I love NLP" 
2.  t2 = "NLP is amazing"
3.  t3 = "This is why nlp is my research interest"

The vocabulary set is: {

  'i',
  'love',
  'nlp',
  'is',
  'amazing',
  'this',
  'why',
  'my',
  'research',
  'interest'

}

 
Each sentence is represented as a vector:

 'doc0': [1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 'doc1': [0, 0, 1, 1, 1, 0, 0, 0, 0, 0],
 'doc2': [0, 0, 1, 2, 0, 1, 1, 1, 1, 1]

The frequency of each word forms the basis of the BoW representation.

 

Result :  

 

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Challenges & Limitations

While BoW is simple and effective, it has some limitations:

1. Loss of Context: Word order is ignored, leading to different meanings being treated the same.
2. Sparsity: Large vocabularies result in high-dimensional vectors with many zero values.
3. Equal Weighting: All words contribute equally, even if some are more important than others.

To overcome these, more advanced techniques such as TF-IDF and word embeddings (Word2Vec, GloVe, FastText) can be employed.

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Next Steps: Moving Beyond BoW

Now that we’ve built BoW, the next step is to improve its effectiveness:

• TF-IDF: A weighting scheme that reduces the impact of common words.
• Word Embeddings: Capturing semantic meaning by learning word relationships.
• Contextual Representations: Using deep learning models like BERT for advanced text understanding.

This marks the completion of the first step in our Lexical Representation journey. The next challenge awaits!

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Conclusion

The Bag of Words model remains a cornerstone in NLP despite its limitations. Implementing it from scratch reinforces a deeper understanding of text vectorization techniques. With this foundation in place, we now venture into semantic-aware representations, where meaning and context play a critical role in text understanding.

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