Distributional Similarity

Quantifies word similarity based on their distribution patterns across text, rather than direct semantic connections. This concept, rooted in the principle that words appearing in similar contexts tend to have related meanings, leverages large text corpora to analyze word usage. By examining how frequently words co-occur with others, it provides a measure of their similarity, enabling the identification of words with parallel usage patterns. This approach is fundamental in computational linguistics and NLP for tasks like synonym detection and semantic analysis, offering insights into the relational dynamics of language without relying solely on predefined semantic relationships.