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.

Popular word embedding models include Word2Vec, GloVe (Global Vectors for Word Representation), FastText, ELMO (Embeddings from Language Models), and BERT (Bidirectional Encoder Representations from Transformers).

• Word2Vec: Word2Vec is a shallow neural network model that learns word embeddings by predicting neighboring words in a large corpus of text.

• GloVe (Global Vectors for Word Representation): GloVe is an unsupervised learning algorithm for obtaining word embeddings by factorizing the word-word co-occurrence matrixA co-occurrence matrix tracks how often pairs of words appear together within a specific context. For each word in a document, we look at a window of surrounding words. For instance, if the window size is set to 3, we consider the 3 words before and the 3 words after the target word. The co-occurrence matrix is a matrix where each value shows how many times a word appears within the context window of another word. This matrix helps to capture the relationship between words based on their proximity in the text..

• FastText: FastText extends Word2Vec by considering subword information to generate word embeddings, enabling it to capture morphological similarities between words.

• ELMo (Embeddings from Language Models): ELMo generates contextualized word embeddings by combining features from a bidirectional language model trained on a large corpus. ...