Distributional

Distributional is an adjective used across disciplines to denote relation to distributions or to patterns of co-occurrence that arise in data. In mathematics and statistics, it typically refers to probability distributions or distribution-valued objects, and to properties, assumptions, or convergence that are described in terms of distributions.

In linguistics and computational linguistics, distributional refers to the distributional hypothesis: words that occur in similar contexts tend to have similar meanings. This underpins distributional semantics and word embedding models such as Latent Semantic Analysis, word2vec, and GloVe, which build semantic representations from large text corpora by analyzing co-occurrence statistics within a context window. The approach is associated with early work by Zellig Harris and was popularized in NLP through neural and matrix factorization methods. Challenges include polysemy, context sensitivity, and high-dimensional sparse data, which are mitigated by weighting schemes, dimensionality reduction, and neural architectures.

In mathematics, distributional can describe properties of distributions in probability theory and statistics, such as distributional assumptions about data or the behavior of random variables in terms of their distributions. In functional analysis, distribution theory (also called the theory of distributions) treats objects called distributions or generalized functions, enabling derivatives and integrals for non-smooth functions. Terms such as distributional derivative and distributional convergence arise from this framework and are foundational to modern analysis.

See also: distribution, probability distribution, distributional semantics, word embedding, generalized function, weak convergence.