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description
Approaches of word representation

🌚 Word Representation

🌚 Word Representation

This document may contain incorrect info 🙄‼ Please open a pull request to fix when you find a one 🌟

  • One Hot Encoding
  • Featurized Representation (Word Embedding)
  • Word2Vec
  • Skip Gram Model
  • GloVe (Global Vectors for Word Representation)

🚀 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 our 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 💔

🎎 Featurized Representation (Word Embedding)

  • Representing words by associating them with features such as gender, age, royal, food, cost, size.... and so on
  • Every feature is represented as a range between [-1, 1]
  • Thus, every word can be represented as a vector of these features
    • The dimension of each vector is related to the number of features that we pick

🔢 Embedded Matrix

For a given word w, the embedding matrix E is a matrix that maps its 1-hot representation $$o_w$$ to its embedding $$e_w$$ as follows:

$$e_w=Eo_w$$

🎀 Advantages

  • Words that have the similar meaning have a similar representation.
  • This model can capture semantic features ✨
  • Vectors are smaller than vectors in one hot representation.

TODO: Subtracting vectors of oppsite words

🔄 Word2Vec

  • Word2vec is a strategy to learn word embeddings by estimating the likelihood that a given word is surrounded by other words.
  • This is done by making context and target word pairs which further depends on the window size we take.
    • Window size: a parameter that looks to the left and right of the context word for as many as window_size words

Creating Context to Target pairs with window size = 2 🙌

Skip Gram Model

The skip-gram word2vec model is a supervised learning task that learns word embeddings by assessing the likelihood of any given target word t happening with a context word c. By noting $$θ_{t}$$ a parameter associated with t, the probability P(t|c) is given by:

$$P(t|c)=\frac{exp(\theta^T_te_c)}{\sum_{j=1}^{|V|}exp(\theta^T_je_c)}$$

Remark: summing over the whole vocabulary in the denominator of the softmax part makes this model computationally expensive

🚀 One Hot Rep. vs Word Embedding

🧤 GloVe

The GloVe model, short for global vectors for word representation, is a word embedding technique that uses a co-occurence matrix X where each $$X_{ij}$$ denotes the number of times that a target i occurred with a context j. Its cost function J is as follows:

$$J(\theta)=\frac{1}{2}\sum_{i,j=1}^{|V|}f(X_{ij})(\theta^T_ie_j+b_i+b'j-log(X{ij}))^2$$

where f is a weighting function such that $$X_{ij}=0$$ ⟹ $$f(X_{ij})$$ = 0. Given the symmetry that e and θ play in this model, the final word embedding e $$e^{(final)}_w$$ is given by:

$$e^{(final)}_w=\frac{e_w+\theta_w}{2}$$

👩‍🏫 Conclusion of Word Embeddings

  • If this is your first try, you should try to download a pre-trained model that has been made and actually works best.
  • If you have enough data, you can try to implement one of the available algorithms.
  • Because word embeddings are very computationally expensive to train, most ML practitioners will load a pre-trained set of embeddings.

🧐 References