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  2. Semantic feature - Wikipedia

    en.wikipedia.org/wiki/Semantic_feature

    The term semantic feature is usually used interchangeably with the term semantic component. [9] Additionally, semantic features/semantic components are also often referred to as semantic properties. [10] The theory of componential analysis and semantic features is not the only approach to analyzing the semantic structure of words. An ...

  3. Statistical semantics - Wikipedia

    en.wikipedia.org/wiki/Statistical_semantics

    He argued that word sense disambiguation for machine translation should be based on the co-occurrence frequency of the context words near a given target word. The underlying assumption that "a word is characterized by the company it keeps" was advocated by J.R. Firth. [2] This assumption is known in linguistics as the distributional hypothesis. [3]

  4. Componential analysis - Wikipedia

    en.wikipedia.org/wiki/Componential_analysis

    Componential analysis is a method typical of structural semantics which analyzes the components of a word's meaning. Thus, it reveals the culturally important features by which speakers of the language distinguish different words in a semantic field or domain (Ottenheimer, 2006, p. 20).

  5. Cohort model - Wikipedia

    en.wikipedia.org/wiki/Cohort_model

    Much evidence in favor of the cohort model has come from priming studies, in which a priming word is presented to a subject and then closely followed by a target word and the subject asked to identify if the target word is a real word or not; the theory behind the priming paradigm is that if a word is activated in the subject's mental lexicon ...

  6. Lexis (linguistics) - Wikipedia

    en.wikipedia.org/wiki/Lexis_(linguistics)

    Collocation: words and their co-occurrences (examples include "fulfill needs" and "fall-back position") Semantic prosody: the connotation words carry ("pay attention" can be neutral or remonstrative, as when a teacher says to a pupil: "Pay attention!"

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    They found that Word2vec has a steep learning curve, outperforming another word-embedding technique, latent semantic analysis (LSA), when it is trained with medium to large corpus size (more than 10 million words). However, with a small training corpus, LSA showed better performance.

  8. Semantic network - Wikipedia

    en.wikipedia.org/wiki/Semantic_network

    The effect of priming on a semantic network linking can be seen through the speed of the reaction time to the word. Priming can help to reveal the structure of a semantic network and which words are most closely associated with the original word. Disruption of a semantic network can lead to a semantic deficit (not to be confused with as ...

  9. Semantic feature-comparison model - Wikipedia

    en.wikipedia.org/wiki/Semantic_feature...

    The cognitive approach consists of two concepts: information processing depends on internal representations, and that mental representations undergo transformations.For the first concept, we could describe an object in a number of ways, with drawings, equations, or verbal descriptions, but it is up to the recipient to have a background understanding of the context to which the object is being ...