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Distributional semantics [1] is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between linguistic items based on their distributional properties in large samples of language data.
Distributionalism can be said to have originated in the work of structuralist linguist Leonard Bloomfield and was more clearly formalised by Zellig S. Harris. [1] [3]This theory emerged in the United States in the 1950s, as a variant of structuralism, which was the mainstream linguistic theory at the time, and dominated American linguistics for some time. [4]
This assumption is known in linguistics as the distributional hypothesis. [3] Emile Delavenay defined statistical semantics as the "statistical study of the meanings of words and their frequency and order of recurrence". [4] "Furnas et al. 1983" is frequently cited as a foundational contribution to statistical semantics. [5]
There are multiple definitions of DisCoCat in the literature, depending on the choice made for the compositional aspect of the model. The common denominator between all the existent versions, however, always involves a categorical definition of DisCoCat as a structure-preserving functor from a category of grammar to a category of semantics, which usually encodes the distributional hypothesis.
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.
Charles J. Fillmore (August 9, 1929 – February 13, 2014) was an American linguist and Professor of Linguistics at the University of California, Berkeley.He received his Ph.D. in Linguistics from the University of Michigan in 1961.
The distribution of a linguistic element is the set of environments in which it occurs, which may be in complementary distribution, contrastive distribution, or free variation with another such element, and which is the basis of distributional semantics
Distributional–relational models were first formalized, [3] [4] as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the ...