 In its broadest sense, statistical semantics is concerned with semantic properties of words, phrases, sentences, and texts, engendered by their distributional characteristics in large text corpora. For example, terms such as cheerful, exuberant, and depressed may be considered semantically similar to the extent that they tend to occur flanked by the same (or in turn similar) nearby words. (For some purposes, such as information retrieval, identifying labels of documents may be used as occurrence contexts.) Through careful distinctions among various occurrence contexts, it  may also be possible to factor similarity into more specific relations such as synonymy, entailment, and antonymy. One basic difference between (standard) logical semantic relations and relations based on distributional similarity is that the latter are a matter of degree. Further, the underlying abstractions are very different, in that statistical semantics does not relate strings to the world, but only to their contexts of occurrence (a notion similar to, but narrower than, Wittgenstein's notion of meaning as use). However, statistical semantics does admit elegant formalizations. Various concepts of similarity and other semantic relations can be captured in terms of vector algebra, by viewing the occurrence frequencies of an expression as values of the components of a vector, with the components corresponding to the distinct contexts of occurrence. In this way, one arrives at a notion of semantics based on metrics and operators in vector spaces, where vector operators can mimic Boolean operators in various ways (Gärdenfors 2000; Widdows 2004; Clarke 2012).