What are vector embeddings?
Vector embeddings, also known as “word embeddings” in the context of natural language processing (NLP), are a type of representation that converts high-dimensional data into a continuous vector space, where the dimensions capture various attributes or semantics of the data.
How vector embeddings work
Vector embeddings are numerical vectors that encode the meaning, relationships, and context of data points to ensure similar items are represented by similar vectors.
Dimensionality reduction: Reduce the complexity of high-dimensional data (such as words, images, or other entities) into lower-dimensional vectors while preserving semantic relationships.
Contextual representation: In NLP, word embeddings capture the context of a word within a text, enabling the understanding of synonyms and the meaning of words based on their usage.
Similarity measurement: Allow for the computation of similarity between data points using measures such as cosine similarity or Euclidean distance. This is useful in tasks like clustering, recommendation, and information retrieval.
Training: Embeddings can be learned through various machine learning techniques, such as neural networks (e.g., Word2Vec, GloVe, BERT), where the model is trained on large datasets to capture the underlying structure and relationships.
Applications: They are used in numerous applications including text classification, sentiment analysis, machine translation, image recognition, and recommendation systems.
Vector embeddings are powerful tools for transforming and representing complex data in a way that facilitates easier analysis and improved performance in various machine learning and AI tasks.
