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  2. Semantic feature - Wikipedia

    en.wikipedia.org/wiki/Semantic_feature

    [1] An individual semantic feature constitutes one component of a word's intention, which is the inherent sense or concept evoked. [2] Linguistic meaning of a word is proposed to arise from contrasts and significant differences with other words. Semantic features enable linguistics to explain how words that share certain features may be members ...

  3. 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).

  4. Feature (linguistics) - Wikipedia

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

    Other types of grammatical features, by contrast, may be relevant to semantics (morphosemantic features), such as tense, aspect and mood, or may only be relevant to morphology (morphological features). Inflectional class (a word's membership of a particular verb class or noun class) is a purely morphological feature, because it is only relevant ...

  5. Lexicology - Wikipedia

    en.wikipedia.org/wiki/Lexicology

    A word is the smallest meaningful unit of a language that can stand on its own, and is made up of small components called morphemes and even smaller elements known as phonemes, or distinguishing sounds. Lexicology examines every feature of a word – including formation, spelling, origin, usage, and definition. [1]

  6. Semantic analysis (linguistics) - Wikipedia

    en.wikipedia.org/wiki/Semantic_analysis...

    In linguistics, semantic analysis is the process of relating syntactic structures, from the levels of words, phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings. It also involves removing features specific to particular linguistic and cultural contexts, to the extent that ...

  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 feature-comparison model - Wikipedia

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

    The semantic feature comparison model is used "to derive predictions about categorization times in a situation where a subject must rapidly decide whether a test item is a member of a particular target category". [1] In this semantic model, there is an assumption that certain occurrences are categorized using its features or attributes of the ...

  9. Statistical semantics - Wikipedia

    en.wikipedia.org/wiki/Statistical_semantics

    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 ] Emile Delavenay defined statistical semantics as the "statistical study of the meanings of words and their frequency and order of recurrence". [ 4 ] "