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Semantic properties or meaning properties are those aspects of a linguistic unit, such as a morpheme, word, or sentence, that contribute to the meaning of that unit.Basic semantic properties include being meaningful or meaningless – for example, whether a given word is part of a language's lexicon with a generally understood meaning; polysemy, having multiple, typically related, meanings ...
A seme is the name for the smallest unit of meaning recognized in semantics, referring to a single characteristic of a sememe. There are five types of sememes: two denotational and three connotational, the latter occurring only in phrase units (they do not reflect the denotation): [1] Denotational 1: Primary denotation, for example "head" (body);
Semantics studies meaning in language, which is limited to the meaning of linguistic expressions. It concerns how signs are interpreted and what information they contain. An example is the meaning of words provided in dictionary definitions by giving synonymous expressions or paraphrases, like defining the meaning of the term ram as adult male sheep. [22]
Lexicology is the branch of linguistics that analyzes the lexicon of a specific language.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.
Seme, the smallest unit of meaning recognized in semantics, refers to a single characteristic of a sememe. These characteristics are defined according to the differences between sememes. The term was introduced by Eric Buyssens [Wikidata] in the 1930s and developed by Bernard Pottier [Wikidata] in the 1960s.
Conceptual semantics is a framework for semantic analysis developed mainly by Ray Jackendoff in 1976. Its aim is to provide a characterization of the conceptual elements by which a person understands words and sentences, and thus to provide an explanatory semantic representation (title of a Jackendoff 1976 paper).
Based on text analyses, semantic relatedness between units of language (e.g., words, sentences) can also be estimated using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus. The evaluation of the proposed semantic similarity / relatedness measures are evaluated through two main ways.