Embedding
An “embedding” in the context of machine learning and data science refers to a way of converting categorical data, like words or items, into vectors of real numbers. This transformation allows complex, high-dimensional data to be used in mathematical models, typically for tasks in natural language processing (NLP), recommendation systems, and more.
In Language (Word Embeddings):
When we talk about embeddings in language, we mean word embeddings. These are representations where each word in a vocabulary is mapped to a vector in a continuous vector space. The idea is that words with similar meanings will be closer to each other in this space than words with different meanings. This is achieved by analyzing the words’ contexts — how they’re used in sentences. Common models for creating word embeddings include Word2Vec, GloVe, and FastText.
For example, in a word embedding space, words like “king” and “queen” might be closer together than “king” and “apple,” reflecting their semantic similarity.
In Other Domains:
While word embeddings are common, the concept applies beyond language. For example:
- Item Embeddings: In recommendation systems, items like movies or products can be embedded into a vector space. Similar items will be closer together, which helps in making recommendations that are “similar” to a user’s past preferences.
- Graph Embeddings: Nodes in a network (like users in a social network) can be embedded such that the geometric relationships between points (vectors) reflect the structure of the network, like which users are more connected.
Why Use Embeddings at all?
- Dimensionality Reduction: Embeddings help reduce the dimensionality of categorical variables, making them more manageable for algorithms to process.
- Semantic Meaning: In text, embeddings capture deeper semantic meaning that isn’t possible with simpler representations like one-hot encoding.
- Generalization: By representing items as points in a continuous space, models can better generalize from seen to unseen data, as they can infer properties from the position in the vector space.
