An embedding, or semantic feature vector, is a sophisticated type of numerical representation used in machine learning (ML) to quantify the meaning and context of a document or text. This representation captures the document’s meaning in a numerical format, which allows search systems to perform mathematical comparisons and calculations on textual information. These vectors can be generated using advanced models like Word2Vec, which maps words or entire documents into a continuous vector space based on their semantic relationships.
Embeddings serve as one of the key data points used by machine learning models to generate an Information Gain (IG) score. In a system tasked with calculating IG, the first step often involves representing each document as such a semantic feature vector. This vector allows a neural network to process the document and compare it against other documents, quantifying the extent of new, meaningful information the document provides relative to what the user has already consumed.
Within the context of semantic keyword research, embeddings form the basis for advanced keyword classification techniques. For example, approaches like Sentence-BERT (sBERT) use embeddings to perform detailed classification and contextual keyword analysis. Because these models rely on the numerical semantic representation of text, they achieve a high degree of semantic precision when mapping keywords to topics, effectively handling nuanced relationships and improving upon less sophisticated methods such as string fuzzy matching. Embeddings are an example of a sophisticated semantic representation, distinct from simpler methods like a bag of words or a histogram.