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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 ...
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).
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]
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]
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 ...
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. 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.
This category can be semantic, including objects such as animals or fruits, or phonemic, including words beginning with a specified letter, such as p, for example. [1] The semantic fluency test is sometimes described as the category fluency test or simply as "freelisting", while letter fluency is also referred to as phonemic test fluency.
This example shows that taxonomic relationships are inherent within semantic networks. The most closely related concepts typically share semantic features, which are determinants of semantic similarity scores. Words with higher similarity scores are more closely related, thus have higher probability of being a close word in the semantic network.