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General Concepts

General Concepts of Sequence Models

👩‍🏫 Notation

In the context of text processing (e.g: Natural Language Processing NLP)
Symbol
Description
$$X^{}$$
The tth word in the input sequence
$$Y^{}$$
The tth word in the output sequence
$$X^{(i)}$$
The tth word in the ith input sequence
$$Y^{(i)}$$
The tth word in the ith output sequence
$$T^{(i)}_x$$
The length of the ith input sequence
$$T^{(i)}_y$$
The length of the ith output sequence

🚀 One Hot Encoding

A way to represent words so we can treat with them easily

🔎 Example

Let's say that we have a dictionary that consists of 10 words (🤭) and the words of the dictionary are:
  • Car, Pen, Girl, Berry, Apple, Likes, The, And, Boy, Book.
Our $$X^{(i)}$$ is: The Girl Likes Apple And Berry
So we can represent this sequence like the following 👀
Car -0)0 ⌉ ⌈ 0 ⌉ ⌈ 0 ⌉ ⌈ 0 ⌉ ⌈ 0 ⌉ ⌈ 0
Pen -1) | 0 | | 0 | | 0 | | 0 | | 0 | | 0 |
Girl -2) | 0 | | 1 | | 0 | | 0 | | 0 | | 0 |
Berry -3) | 0 | | 0 | | 0 | | 0 | | 0 | | 1 |
Apple -4) | 0 | | 0 | | 0 | | 1 | | 0 | | 0 |
Likes -5) | 0 | | 0 | | 1 | | 0 | | 0 | | 0 |
The -6) | 1 | | 0 | | 0 | | 0 | | 0 | | 0 |
And -7) | 0 | | 0 | | 0 | | 0 | | 1 | | 0 |
Boy -8) | 0 | | 0 | | 0 | | 0 | | 0 | | 0 |
Book -9)0 ⌋ ⌊ 0 ⌋ ⌊ 0 ⌋ ⌊ 0 ⌋ ⌊ 0 ⌋ ⌊ 0
By representing sequences in this way we can feed out data to neural networks ✨

🙄 Disadvantage

  • If our dictionary consists of 10,000 words so each vector will be 10,000 dimensional 🤕
  • This representation can not capture semantic features 💔