🧺 Language as a Bag-of-Words
Conceptual Walkthrough with “My cat is cute”
🪄 What is Bag-of-Words?
Bag-of-Words is one of the simplest ways to turn language into numbers. It treats a sentence like a collection of words, completely ignoring grammar or order — just like tossing words into a bag and counting what’s inside. 🛍️
It’s a foundational idea that helps bridge the gap between raw text and the vector-based world of machine learning.
📝 Step-by-Step Explanation
🧾 Step 1: Input Sentence
Let's start with a simple sentence:
"My cat is cute"
This is the input we'll transform into a numerical format.
🪓 Step 2: Tokenization
Before we can analyze the sentence, we break it up by spaces (called whitespace tokenization). This gives us the individual tokens (words):
["my", "cat", "is", "cute"]
Each word becomes a building block the model can count.
📚 Step 3: Build a Vocabulary
Now we need a predefined list of all the words we care about — this is our vocabulary.
Imagine it looks like this:
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["that", "is", "a", "cute", "dog", "my", "cat"]
Each word in the vocabulary has a fixed position — this is important because our vector will match this order.
🧮 Step 4: Count Words (Vector Representation)
Now we look at our sentence: "my cat is cute".
For each word in the vocabulary, we count how many times it appears in the sentence:
| Vocabulary Word | Appears in Sentence? | Count |
|---|---|---|
| that | ❌ | 0 |
| is | ✅ | 1 |
| a | ❌ | 0 |
| cute | ✅ | 1 |
| dog | ❌ | 0 |
| my | ✅ | 1 |
| cat | ✅ | 1 |
Now we can turn this into a vector:
Bag-of-Words Vector = [0, 1, 0, 1, 0, 1, 1]
This is a 7-dimensional vector. Each number matches the count of the corresponding vocabulary word.
💡 Why Is This Useful?
✅ Easy to implement
✅ Captures word presence and frequency
✅ Works well for simpler tasks (e.g. spam detection, topic classification)
🚫 Limitations
-
❌ Ignores word order
“My cat is cute” vs “Cute cat my is” = same BoW vector
-
❌ Doesn’t understand meaning
“Good” and “Excellent” are unrelated in this model
-
❌ Sparse vectors
Most real-world vocabularies are huge → long, mostly-zero vectors
📦 TL;DR – How It Works
- Define your vocabulary (fixed size).
- Tokenize your input text.
- Count how many times each vocab word appears.
- Turn those counts into a vector — that’s your input!