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In language, word order is not decoration. Move the words around and the whole message can flip to something new.

Dog bites man and man bites dog share the exact same words, yet only order tells you who did the biting.

A bag-of-words model just counts tokens, so it throws away order. To it, both biting sentences look completely identical.

Once you reduce text to word counts, you cannot recover the original sequence. The story collapses into a frequency table.

Notice how not good means the opposite of good. The word not only works because of where it sits in the sentence.

Some clues sit far apart. The film I watched last night was great links great back to film across many tokens.

You read left to right, building meaning step by step. Text is a sequence, and earlier words shape how later ones land.

In Python you can keep that order simply by splitting into a list, where the index reflects each word position.

Two sentences can have matching counts but opposite meaning. A model that ignores position can never tell them apart.

To capture order we need models that read text as a flowing sequence, remembering what came before each new word.

This is exactly the gap recurrent neural networks fill. They process tokens one at a time, in order, instead of all at once. 🧠

Why do bag-of-words models miss meaning when order changes?

Word order changes meaning, but count-based models ignore it. That limit is why we move to sequence-aware models next. 🎯