Search results
Results From The WOW.Com Content Network
[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 ...
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 ] "
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).
Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing [2] and word-sense disambiguation. [3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or ...
Research has shown reliable spacing effects in cued-memory tasks under incidental learning conditions, where semantic analysis is encouraged through orienting tasks (Challis, 1993; Russo & Mammaralla, 2002). Challis found a spacing effect for target words using a frequency estimation task after words were incidentally analyzed semantically.
It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans. [1]: 93- Understanding the semantics of a text is symbol grounding: if language is grounded, it is equal to recognizing a machine-readable meaning ...
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 ...
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 ...