What are common types of embeddings?

Common types of embeddings include word, sentence, image, graph, and categorical embeddings. These techniques convert complex data into numerical vectors, enabling machine learning models to process patterns efficiently. Each type addresses specific data structures and use cases, making them foundational tools in modern AI systems. Below, we’ll explore key examples and their practical applications.

Word embeddings represent individual words as dense vectors. For example, Word2Vec uses two architectures: Continuous Bag of Words (CBOW) predicts a word from its context, while Skip-Gram does the inverse. GloVe captures global word co-occurrence statistics, often performing better on semantic tasks. Contextual embeddings like BERT generate dynamic representations based on surrounding text—the word “bank” in “river bank” versus “bank account” gets distinct vectors. These embeddings power tasks like sentiment analysis, named entity recognition, and machine translation. Sentence and document embeddings, such as Doc2Vec or Universal Sentence Encoder, extend word-level techniques to longer text by aggregating word vectors or training on sentence-level tasks, enabling semantic similarity comparisons or clustering.

Image embeddings transform pixels into feature vectors using convolutional neural networks (CNNs). Models like ResNet or VGG16 pre-trained on ImageNet extract high-level features (edges, textures) useful for tasks like object detection or image retrieval. Graph embeddings (e.g., Node2Vec) represent nodes in networks (social networks, recommendation graphs) as vectors by preserving structural relationships. Categorical embeddings handle discrete data (user IDs, product categories) in tabular datasets, often replacing one-hot encoding to reduce dimensionality and capture latent relationships—common in recommendation systems.

Choosing the right embedding depends on data type and task. Pre-trained embeddings (BERT, ResNet) save training time but may need fine-tuning. Custom embeddings adapt better to niche domains. For instance, a recommendation system might combine categorical embeddings for user IDs with graph embeddings for social connections. Understanding these options helps developers balance efficiency, accuracy, and resource constraints effectively.