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Intro to Vector Embeddings

What Are Vector Embeddings?​

Vector embeddings, often referred to simply as embeddings, are numerical representations of data that capture the essence or semantic meaning of that data in a multi-dimensional space. In the context of natural language processing (NLP), embeddings are used to convert words, sentences, or even entire documents into vectors (arrays of numbers) that can be easily processed by machine learning models.

The key idea behind embeddings is to transform complex, high-dimensional data (like text) into a lower-dimensional vector space while preserving important relationships and similarities between the data points.

How Vector Embeddings Work​

  1. Dimensionality Reduction: Embeddings reduce the dimensionality of data. For example, a word might be represented by hundreds or thousands of dimensions in its raw form (like in one-hot encoding). An embedding reduces this to a more manageable size, such as a 100-dimensional vector, while still capturing the word's meaning.

  2. Semantic Relationships: The position of vectors in the embedding space reflects the relationships between the data points. For example, words with similar meanings (like "king" and "queen") will have vectors that are close to each other in the embedding space.

Example of Vector Embeddings​

Consider the following words: "king," "queen," "man," and "woman."

In a 2-dimensional space, an embedding might represent these words as follows:

  • "king" → [0.8, 0.5]
  • "queen" → [0.7, 0.6]
  • "man" → [0.9, 0.3]
  • "woman" → [0.6, 0.7]

Visualization:​

If we plot these vectors on a 2D graph, we might observe the following relationships:

  • The vectors for "king" and "queen" are close to each other, indicating that they are semantically related (both are royalty).
  • Similarly, "man" and "woman" are close to each other, indicating a relationship (both are humans with gender distinctions).
  • The difference between "king" and "man" is similar to the difference between "queen" and "woman." This reflects the gender relationship between these pairs.

Practical Example in NLP:​

Suppose you have the sentence: "The cat sat on the mat." An embedding model would convert this sentence into a series of vectors, one for each word, that captures the meaning of each word in relation to the others.

  • "The" → [0.01, 0.02, 0.03, ..., 0.10]
  • "cat" → [0.5, 0.6, 0.7, ..., 0.9]
  • "sat" → [0.4, 0.3, 0.2, ..., 0.8]
  • "on" → [0.15, 0.25, 0.35, ..., 0.45]
  • "mat" → [0.6, 0.5, 0.4, ..., 0.3]

In this vector space, words with similar meanings or usage in similar contexts would have vectors that are close to each other.

Use Case: Chatbot Applications​

In a chatbot, embeddings can be used to understand the user’s input and retrieve relevant information or generate a response. For instance, if a user types "Tell me about the weather," the embedding of this sentence would be compared with embeddings of potential responses stored in the system. The response with the closest vector (most similar meaning) would be selected.

Summary​

Vector embeddings are a powerful tool in NLP for representing text data in a way that preserves semantic meaning while reducing dimensionality. By transforming words, sentences, or documents into vectors in a multi-dimensional space, embeddings make it easier for machine learning models to process and understand language, leading to better performance in tasks like text classification, sentiment analysis, and chatbots.