From frequency to meaning: Vector space models of semantics

This paper introduces vector space models as a way to represent the meaning of words. It explains how these models use word co-occurrences to create semantic representations, capturing relationships between words based on how often they appear together in text. The paper discusses different techniques for constructing and evaluating these models, highlighting their ability to capture semantic similarities and solve various language-related tasks, paving the way for many AI applications in natural language processing and information retrieval.