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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]
The distributional hypothesis is the basis for statistical semantics. Although the Distributional Hypothesis originated in linguistics, [4] [5] it is now receiving attention in cognitive science especially regarding the context of word use. [6]
In linguistics, Immediate Constituent Analysis (ICA) is a syntactic theory which focuses on the hierarchical structure of sentences by isolating and identifying the constituents. While the idea of breaking down sentences into smaller components can be traced back to early psychological and linguistic theories, ICA as a formal method was ...
In morphology, two morphemes are in contrastive distribution if they occur in the same environment, but have different meanings.. For example, in Korean, noun phrases are followed by one of the various markers that indicate syntactic role: /-ka/, /-i/, /-(l)ul/, etc. /-ka/ and /-i/ are in complementary distribution.
The basic principle of Distributed Morphology is that there is a single generative engine for the formation of both complex words and complex phrases: there is no division between syntax and morphology and there is no Lexicon in the sense it has in traditional generative grammar.
In linguistics, complementary distribution (as distinct from contrastive distribution and free variation) is the relationship between two different elements of the same kind in which one element is found in one set of environments and the other element is found in a non-intersecting (complementary) set of environments.
Statistical semantics is a subfield of computational semantics, which is in turn a subfield of computational linguistics and natural language processing. Many of the applications of statistical semantics (listed above) can also be addressed by lexicon -based algorithms, instead of the corpus -based algorithms of statistical semantics.
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.