Semantics Totally Explained

Everything about Semantic totally explained

Semantics is the study of meaning in communication. The word derives from Greek σημαντικός (semantikos), "significant", from σημαίνω (semaino), "to signify, to indicate" and that from σήμα (sema), "sign, mark, token". In linguistics it's the study of interpretation of signs as used by agents or communities within particular circumstances and contexts. It has related meanings in several other fields.
Semanticists differ on what constitutes meaning in an expression. For example, in the sentence, "John loves a bagel", the word bagel may refer to the object itself, which is its literal meaning or denotation, but it may also refer to many other figurative associations, such as how it meets John's hunger, etc., which may be its connotation. Traditionally, the formal semantic view restricts semantics to its literal meaning, and relegates all figurative associations to pragmatics, but this distinction is increasingly difficult to defend.
This traditional view of semantics, as an innate finite meaning inherent in a lexical unit that can be composed to generate meanings for larger chunks of discourse, is now being fiercely debated in the emerging domain of cognitive linguistics and also in the non-Fodorian camp in Philosophy of Language. The challenge is motivated by

factors internal to language, such as the problem of resolving indexical or anaphora (for example this x, him, last week). In these situations "context" serves as the input, but the interpreted utterance also modifies the context, so it's also the output. Thus, the interpretation is necessarily dynamic and the meaning of sentences is viewed as context-change potentials instead of propositions.

factors external to language, for example language isn't a set of labels stuck on things, but "a toolbox, the importance of whose elements lie in the way they function rather than their attachments to things.". However, the colours implied in phrases such as "red wine" (very dark), and "red hair" (coppery), or "red soil", or "red skin" are very different. Indeed, these colours by themselves wouldn't be called "red" by native speakers. These instances are contrastive, so "red wine" is so called only in comparison with the other kind of wine (which also isn't "white" for the same reasons). This view goes back to de Saussure: ť Each of a set of synonyms like redouter ('to dread'), craindre ('to fear'), avoir peur ('to be afraid') has its particular value only because they stand in contrast with one another. No word has a value that can be identified independently of what else is in its vicinity. and may go back to earlier Indian views on language, especially the Nyaya view of words as indicators and not carriers of meaning.
An attempt to defend a system based on propositional meaning for semantic underspecification can be found in the Generative Lexicon model of James Pustejovsky, who extends contextual operations (based on type shifting) into the lexicon. Thus meanings are generated on the fly based on finite context.

Prototype theory

Another set of concepts related to fuzziness in semantics is based on prototypes. The work of Eleanor Rosch and George Lakoff in the 1970s led to a view that natural categories are not characterizable in terms of necessary and sufficient conditions, but are graded (fuzzy at their boundaries) and inconsistent as to the status of their constituent members.
Systems of categories are not objectively "out there" in the world but are rooted in people's experience. These categories evolve as learned concepts of the world — meaning isn't an objective truth, but a subjective construct, learned from experience, and language arises out of the "grounding of our conceptual systems in shared embodiment and bodily experience". A corollary of this is that the conceptual categories (for example the lexicon) won't be identical for different cultures, or indeed, for every individual in the same culture. This leads to another debate (see the Whorf-Sapir hypothesis or Eskimo words for snow).

Computer science

In computer science, considered in part as an application of mathematical logic, semantics reflects the meaning of programs or functions.
In this regard, semantics permits programs to be separated into their syntactical part (grammatical structure) and their semantic part (meaning). For instance, the following statements use different syntaxes (languages), but result in the same semantic:

x += y; (C, Java, etc.)

x := x + y; (Pascal)

Let x = x + y;

x = x + y (various BASIC languages) Generally these operations would all perform an arithmetical addition of 'y' to 'x' and store the result in a variable 'x'.
Semantics for computer applications falls into three categories:

Operational semantics: The meaning of a construct is specified by the computation it induces when it's executed on a machine. In particular, it's of interest how the effect of a computation is produced.

Denotational semantics: Meanings are modelled by mathematical objects that represent the effect of executing the constructs. Thus only the effect is of interest, not how it's obtained.

Axiomatic semantics: Specific properties of the effect of executing the constructs as expressed as assertions. Thus there may be aspects of the executions that are ignored. The Semantic Web refers to the extension of the World Wide Web through the embedding of additional semantic metadata; s.a. Web Ontology Language (OWL).

Psychology

In psychology, semantic memory is memory for meaning, in other words, the aspect of memory that preserves only the gist, the general significance, of remembered experience, while episodic memory is memory for the ephemeral details, the individual features, or the unique particulars of experience. Word meaning is measured by the company they keep; the relationships among words themselves in a semantic network. In a network created by people analyzing their understanding of the word (such as Wordnet) the links and decomposition structures of the network are few in number and kind; and include "part of", "kind of", and similar links. In automated ontologies the links are computed vectors without explicit meaning. Various automated technologies are being developed to compute the meaning of words: latent semantic indexing and support vector machines as well as natural language processing, neural networks and predicate calculus techniques.

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