Embedding Models for Different Data Types

Embedding models

Embedding models are a cornerstone in machine learning and artificial intelligence. They offer a mechanism to represent raw data in a structured and interpretable format. By transforming data into continuous vector spaces, embedding models enable algorithms to capture intricate relationships and semantic nuances inherent in the underlying information.

In the following sections, we’ll learn about different types of embedding models tailored to different data modalities.

Word embedding models

Word embeddings are numerical representations of words in a continuous vector space. These embeddings capture semantic relationships between words based on their usage in a given text corpus. Word embeddings facilitate natural language processing tasks by enabling algorithms to understand the meaning and context of words in a more meaningful and structured manner by representing words as dense vectors in a continuous vector space. Each dimension in an embedding vector captures some aspect of the word’s meaning, making the representation more compact and semantically rich. Word embeddings are often used as input features for various NLP tasks, such as sentiment analysis, language translation, text classification, and named entity recognition.