Semantics (σημαντικός 'significant') is the study of reference, meaning, or truth. The term can be used to refer to subfields of several distinct disciplines, including philosophy, linguistics, and computer science.

Semantics focuses on understanding how words, phrases, sentences, and entire texts convey meaning and how this meaning is interpreted by speakers and listeners. Semantics plays a fundamental role in our ability to communicate and comprehend language.

At its core, semantics explores the relationship between words or linguistic expressions and the concepts or ideas they represent. It seeks to answer questions such as: What does a particular word mean? How are words combined to create meaningful sentences? How do we understand the meaning of a sentence in context?

There are several key concepts and components within the field of semantics:

1. Word Meaning: Semantics investigates how individual words acquire meaning and how their meanings are represented in the mental lexicon of speakers. This includes examining the various aspects of word meaning, such as lexical semantics (the study of word definitions and relationships), connotation (the emotional or evaluative associations of a word), and denotation (the literal or dictionary definition of a word).
2. Sentence Meaning: Semantics explores how the meanings of individual words combine to form meaningful sentences. It investigates the rules and principles that govern sentence structure and the way in which words interact with each other to convey specific meanings. This includes studying syntactic structures, grammatical categories, and the role of function words.
3. Pragmatics: While closely related to semantics, pragmatics focuses on the study of meaning in context. It examines how meaning is influenced by factors such as the speaker's intentions, the context of the conversation, and the shared background knowledge between the speaker and listener. Pragmatics helps to explain how meaning can go beyond the literal interpretation of words and take into account the speaker's implied meaning or the speaker's meaning.
4. Truth-Conditional Semantics: This approach to semantics places an emphasis on truth and meaning. It seeks to analyze the meaning of a sentence by defining the conditions under which it would be considered true or false. Truth-conditional semantics involves breaking down the meaning of a sentence into its basic components and examining how these components interact to determine truth-value.
5. Semantic Relations: Semantics investigates the relationships between words and linguistic expressions. This includes studying semantic relations such as synonymy (words with similar meanings), antonymy (words with opposite meanings), hyponymy (words that are more specific versions of a general term), and meronymy (words that represent a part-whole relationship).
6. Cross-linguistic Semantics: Semantics is not limited to a single language but is a comparative field that examines meaning across different languages. It explores the similarities and differences in how languages encode and express meaning, highlighting the universality and cultural specificity of certain concepts.

Semantics has applications in various fields, including natural language processing (NLP), computational linguistics, machine translation, and language education. It helps in developing algorithms and systems that can understand and generate human language, as well as in teaching language learners about the meanings and usage of words and expressions.

In summary, semantics is the study of meaning in language, encompassing the analysis of word meaning, sentence meaning, meaning in context, and the relationships between words and expressions. It is a rich and multifaceted field that contributes to our understanding of how language conveys meaning and how we interpret and comprehend linguistic messages.

Semantic ambiguity is an expression that is semantically ambiguous when it can have multiple meanings. The higher the number of synonyms a word has, the higher the degree of ambiguity. Like other kinds of ambiguity, semantic ambiguities are often clarified by context or by prosody. One's comprehension of a sentence in which a semantically ambiguous word is used is strongly influenced by the general structure of the sentence. The language itself is sometimes a contributing factor in the overall effect of semantic ambiguity, in the sense that the level of ambiguity in the context can change depending on whether or not a language boundary is crossed.

Lexical ambiguity is a subtype of semantic ambiguity where a word or morpheme is ambiguous. When a lexical ambiguity results from a single word having two senses, it is called polysemy. For instance, the English "foot" is polysemous since in general it refers to the base of an object, but can refer more specifically to the foot of a person or the foot of a pot. When an ambiguity instead results from two separate words which happen to be pronounced the same way, it is called homonymy. For instance, the English word "row" can denote the action of rowing or an arrangement of objects. In practice, polysemy and homonymy can be difficult to distinguish.

• Phrases and sentences can also be semantically ambiguous, particularly when there are multiple ways of semantically combining their subparts.

Semantic architecture envisions enabling the architecture community to unambiguously capture, catalog, communicate, preserve, and interoperably exchange semantics of their architectures, thus making architecture descriptions true assets.

The overall goals of the semantic architecture are

• to define a formal and informal semantic way of representing architecture intended to be both human and machine-readable
• to describe a system architecture at a high level of abstraction
• to support the automatic generation of software architecture models
• to permit analysis of architectural quality attributes
• to provide a repository of patterns expressed utilizing the semantic web standards

In order to achieve these goals, the architecture community and industry need to define

• a common architecture description language
• an ontology for architecture data models
• a set of tools for capturing, querying, and visualizing all aspects and viewpoints of an architecture

The tooling or toolkits for semantic architecture should

• be suitable for communicating architecture to all stakeholders
• supports architecture creation, refinement, evaluation, and validation of quality attributes
• provides a basis for further implementation
• allows the architecture community to exchange semantics of architectural styles and patterns in an interoperable fashion

Semantic analysis is the process of relating syntactic structures, from the levels of 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 such a project is possible. The elements of idiom and figurative speech, being cultural, are often also converted into relatively invariant meanings in semantic analysis. Semantics, although related to pragmatics, is distinct in that the former deals with word or sentence choice in any given context, while pragmatics considers the unique or particular meaning derived from context or tone. To reiterate in different terms, semantics is about universally coded meaning, and pragmatics is the meaning encoded in words that are then interpreted by an audience.

Semantic analysis can begin with the relationship between individual words. This requires an understanding of lexical hierarchy, including hyponymy and hypernymy, meronomy, polysemy, synonyms, antonyms, and homonyms. It also relates to concepts like connotation (semiotics) and collocation, which is the particular combination of words that can be or frequently are surrounding a single word. This can include idioms, metaphors, and similes, like, "silver as a devil."

• With the availability of enough material to analyze, semantic analysis can be used to catalog and trace the style of writing of specific authors.

Semantic analysis (computational) within applied linguistics and computer science, is a composite of semantic analysis and computational components. Semantic analysis refers to a formal analysis of meaning, and computational refers to approaches that in principle support effective implementation in objects, elements, and chaos.

Semantic change (also semantic shift, semantic progression, semantic development, or semantic drift) is a form of language change regarding the evolution of word usage—usually to the point that the modern meaning is radically different from the original usage. In diachronic (or historical) linguistics, semantic change is a change in one of the meanings of a word. Every word has a variety of senses and connotations, which can be added, removed, or altered over time, often to the extent that cognates across space and time have very different meanings. The study of semantic change can be seen as part of etymology, onomasiology, semasiology, and semantics.

A semantic class contains words that share a semantic feature. For example, within nouns, there are two sub-classes, concrete nouns, and abstract nouns. Concrete nouns include people, plants, animals, materials, and objects while abstract nouns refer to concepts such as qualities, actions, and processes. According to the nature of the noun, they are categorized into different semantic classes. Semantic classes may intersect. The intersection of male and young can be boy.

A semantic decomposition is an algorithm that breaks down the meanings of phrases or concepts into less complex concepts. The result of a semantic decomposition is a representation of meaning. This representation can be used for tasks, such as those related to quantum artificial intelligence or quantum machine learning. Semantic decomposition is common in natural language processing applications.

• The basic idea of a semantic decomposition is taken from learning skills, where words are explained using other words. It is based on the Meaning-text theory. The meaning-text theory is used as a theoretical linguistic framework to describe the meaning of concepts with other concepts.

Semantic dictionary preserves the full semantic context of source programs while adding further information that can be used for accelerating the speed of code generation.

• In an elementary form, the dictionary entries represent nodes that describe the actions of the program, as an abstract syntax tree in tabular form.
• It uses an intermediate representation (IR), that is based on the encoded abstract syntax tree and symbol table of a program.

Semantic discord is a situation where two parties disagree on the definition of a word(s) that is essential to communicating or formulating the concept(s) being discussed. That is to say, the two parties basically understand two different meanings for the word, or they associate the word with different concepts. Such discord can lead to a semantic dispute, a disagreement that arises if the parties involved disagree about the definition of a word or phrase. Consequently, their disagreement on these definitions explains why there is a dispute at all.

It is sometimes held that semantic disputes are not genuine disputes at all, but very often they are regarded as perfectly genuine, e.g., in philosophy. It is also sometimes held that when a semantic dispute arises, the focus of the debate should switch from the original thesis to the meaning of the terms of which there are different definitions (understandings, concepts, etc.). Semantic disputes can result in the logical fallacy of equivocation.

Any word or instance of communication that has its effectiveness reduced due to semantic discord is said to be semantically loaded, i.e., the information has semantic content and is "propositionally structured," wherein implicit beliefs are tacit.

A semantic domain is a specific place that shares a set of meanings, or a language that holds its meaning, within the given context of the place.

In lexicography, a semantic domain or semantic field is defined as "an area of meaning and the words used to talk about it. Semantic domains are the foundational concept for the initial stages of vernacular dictionary-building projects.

In the social sciences, the concept of semantic domains stemmed from the ideas of cognitive anthropology. The quest was originally to see how the words that groups of humans use to describe certain things are relative to the underlying perceptions and meanings that those groups share. Ethnosemantics became the field that concentrated on the study of these semantic domains, and more specifically the study of how categorization and context of words and groups of words reflected the ways that different Fictions, Nonfictions, Transfictions, Fanfictions, Metafictions, Patafictions, Interfictions, Personal Fictions, Impersonal Fictions, Incompatibilism Fictions, Impossible Fictions, Speculative Fictions, Xenofictions, Universal Genres, Universal Tropes, Paratexts, Memetic Fictions, Transformation Fictions, and a priori and a posteriori Fictions categorize words into speech and assign meaning to their language.

Semantic equivalence is a declaration that two data elements from different vocabularies contain data that has similar meanings. There are three types of semantic equivalence statements:

• Class or concept equivalence. A statement that two high-level concepts have similar or equivalent meanings.
• Property or attribute equivalence. A statement that two properties, descriptors, or attributes of classes have similar meanings.
• Instance equivalence. A statement that two instances of data are the same or refer to the same instance.

Semantic externalism (the opposite of semantic internalism) is the view that the meaning of a term is determined, in whole or in part, by factors external to the speaker. According to an externalist position, one can claim without contradiction that two speakers could be in exactly the same brain state at the time of an utterance, and yet mean different things by that utterance -- that is, at the least, that their terms could pick out different referents.

A semantic feature is a component of the concept associated with a lexical item ('possibility' + 'nothingness' = 'totality'). More generally, it can also be a component of the concept associated with any grammatical unit, whether composed or not. An individual semantic feature constitutes one component of a word's intention, which is the inherent sense or concept evoked. The 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 of the same semantic domain. Correspondingly, the contrast in meanings of words is explained by diverging semantic features.

The analysis of semantic features is utilized in the field of linguistic semantics, more specifically the subfields of lexical semantics, and lexicology. One aim of these subfields is to explain the meaning of a word in terms of its relationships with other words. In order to accomplish this aim, one approach is to analyze the internal semantic structure of a word as composed of a number of distinct and minimal components of meaning. This approach is called componential analysis, also known as semantic decomposition. Semantic decomposition allows any given lexical item to be defined based on minimal elements of meaning, which are called semantic features. The term semantic feature is usually used interchangeably with the term semantic component. Additionally, semantic features/semantic components are also often referred to as semantic properties.

• The theory of componential analysis and semantic features is not the only approach to analyzing the semantic structure of words. An alternative direction of research that contrasts with componential analysis is prototype semantics.

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". In this semantic model, there is an assumption that certain occurrences are categorized using the features or attributes of the two subjects that represent the part and the group. A statement often used to explain this model is "a devil is an angel". The meaning of the words devil and angel are stored in the memory by virtue of a list of features that can be used to ultimately define their categories, although the extent of their association with a particular category varies.

A semantic field is a lexical set of words grouped semantically (by meaning) that refers to a specific subject.

Words in a semantic field are not necessarily synonymous but are all used to talk about the same general phenomenon. Synonymy requires the sharing of a sememe or seme, but the semantic field is a larger area surrounding those. A meaning of a word is dependent partly on its relation to other words in the same conceptual area.

The Semantic folding theory describes a procedure for encoding the semantics of natural language text in a semantically grounded binary representation. This approach provides a framework for modeling how language data is processed by the neocortex.

Semantic heterogeneity is when database schema or datasets for the same domain are developed by independent parties, resulting in differences in the meaning and interpretation of data values. Beyond structured data, the problem of semantic heterogeneity is compounded due to the flexibility of semi-structured data and various tagging methods applied to documents or unstructured data. Semantic heterogeneity is one of the more important sources of differences in heterogeneous datasets. Yet, for multiple data sources to interoperate with one another, it is essential to reconcile these semantic differences. Decomposing the various sources of semantic heterogeneities provides a basis for understanding how to map and transform data to overcome these differences.

Semantic holism is a theory in the philosophy of language to the effect that a certain part of language, be it a term or a complete sentence, can only be understood through its relations to a (previously understood) larger segment of language. There is a substantial controversy, however, as to exactly what the larger segment of language in question consists of.

Semantic integration is the process of interrelating information from diverse sources, for example, calendars and to-do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including social graphs), and search results derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value.

A semantic lexicon is a digital dictionary of words labeled with semantic classes so associations can be drawn between words that have not previously been encountered. Semantic lexicons are built upon semantic networks, which represent the semantic relations between words. The difference between a semantic lexicon and a semantic network is that a semantic lexicon has definitions for each word or a "gloss".

Semantic lexicons are made up of lexical entries. These entries are not orthographic, but semantic, eliminating issues of homonymy and polysemy. These lexical entries are interconnected with semantic relations, such as hyperonymy, hyponymy, meronymy, or troponymy. Synonymous entries are grouped together in what the world calls "synsets". Most semantic lexicons are made up of four different "sub-nets": nouns, verbs, adjectives, and adverbs.

A semantic loan is a process of borrowing semantic meaning (rather than lexical items) from another language, very similar to the formation of calques. In this case, however, the complete word in the borrowing language already exists; the change is that its meaning is extended to include another meaning its existing translation has in the lending language. Calques, loanwords, and semantic loans are often grouped roughly under the phrase "borrowing". Semantic loans often occur when two languages are in close contact, and take various forms. The source and target word may be cognates, which may or may not share any contemporary meaning in common; they may be an existing loan translation or parallel construction (compound of corresponding words); or they may be unrelated words that share an existing meaning.

Semantic memory refers to general world knowledge that humans have accumulated throughout their lives. This general knowledge (word meanings, concepts, facts, and ideas) is intertwined with experience and dependent on culture. We can learn about new concepts by applying our knowledge learned from things in the past.

• Semantic memory is distinct from episodic memory, which is our memory of experiences and specific events that occur during our lives, from which we can recreate at any given point.
• Semantic memory and episodic memory are both types of explicit memory (or declarative memory), that is, the memory of facts or events that can be consciously recalled and "declared." The counterpart to declarative or explicit memory is nondeclarative memory or implicit memory.

Semantic overload occurs when a word or phrase has more than one meaning and is used in ways that convey meaning based on its divergent constituent concepts. Semantic overload is related to the linguistic concept of polysemy. Overloading is related to the psychological concept of information overload, and the computer science concept of operator overloading. A term that is semantically overloaded is a kind of "overloaded expression" in language that causes a certain small degree of "information overload" in the receiving audience.

• Meanings associated with a semantically overloaded word have different qualities: those the word itself refers directly to, and other meanings inferred from its use in context.

Semantic phonology is a model for sign language "phonology". This contains the parameter which under other models is analyzed as handshape is in fact the subject (or, as argued the absolutive—i.e. subject of intransitive verbs and object of transitive verbs) and what is the parameter which is analyzed usually as movement represents the verb. Thus, even lexical signs represent full predictions.

Semantic primes or semantic primitives are a set of semantic concepts that are argued to be innately understood by all people but impossible to express in simpler terms. They represent words or phrases that are learned through practice but cannot be defined concretely.

Semantic primes represent universally meaningful concepts, but to have meaningful messages or statements, such concepts must combine in a way that they themselves convey meaning. Such meaningful combinations, in their simplest form as sentences, constitute the syntax of the language.

Semantic processing is the stage of language processing that occurs after one hears a word and encodes its meaning: the mind relates the word to other words with similar meanings. Once a word is perceived, it is placed in a context mentally that allows for deeper processing. Therefore, semantic processing produces memory traces that last longer than those produced by shallow processing, since shallow processing produces fragile memory traces that decay rapidly. Semantic processing is the deepest level of processing and it requires the listener to think about the meaning of the cue.

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; ambiguity, having meanings which aren't necessarily related; an anomaly, where the elements of a unit are semantically incompatible with each other, although possibly grammatically sound. Beyond the expression itself, there are higher-level semantic relations that describe the relationship between units: these include synonymy, antonymy, and hyponymy.

Semantic satiation is a psychological phenomenon in which repetition causes a word or phrase to temporarily lose meaning for the listener, who then perceives the speech as repeated meaningless sounds. Extended inspection or analysis (staring at the word or phrase for a long time) in place of repetition also produces the same effect.

Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings).

A semantic theory of truth is a theory of truth in the philosophy of language that holds that truth is a property of sentences.

Semantic translation is the process of using semantic information to aid in the translation of data from one representation or data model to another representation or data model. Semantic translation takes advantage of semantics that associates meaning with individual data elements in one dictionary to create an equivalent meaning in a second system.

The semantic translation should be differentiated from data mapping tools that do the simple one-to-one translation of data from one system to another without actually associating meaning with each data element.

Semantic translation requires that data elements in the source and destination systems have "semantic mappings" to a central registry or registries of data elements. The simplest mapping is of course where there is equivalence. There are three types of Semantic equivalence:

• Class Equivalence - indicating that class or "concepts" are equivalent.
• Property Equivalence - indicating that two properties are equivalent.
• Instance Equivalence - indicating that two individual instances of objects are equivalent.

Semantic unification is the process of unifying lexically different concept representations that are judged to have the same semantic content (i.e., meaning). In business processes, conceptual semantic unification is defined as "the mapping of two expressions onto an expression in an exchange format which is equivalent to the given expression".

Semanticity refers to the use of arbitrary or nonarbitrary signals to transmit meaningful messages.

The Semantics of Logic or Formal Semantics is the study of the semantics, or interpretations, of formal and (idealizations of) natural languages usually trying to capture the pre-theoretic notion of entailment.

The main modern approaches to semantics for formal languages are the following:

• The archetype of model-theoretic semantics is the idea that the meaning of the various parts of the propositions are given by the possible ways we can give a recursively specified group of interpretation functions from them to some predefined mathematical domains: an interpretation of first-order predicate logic is given by a mapping from terms to a universe of individuals, and a mapping from propositions to the truth values "true" and "false". Model-theoretic semantics provides the foundations for an approach to the theory of meaning known as truth-conditional semantics.
• Proof-theoretic semantics associates the meaning of propositions with the roles that they can play in inferences.
• Truth-value semantics (also commonly referred to as substitutional quantification) encompasses the truth conditions for quantified formulas that are given purely in terms of truth with no appeal to domains whatsoever (and hence its name truth-value semantics).
• Game semantics or game-theoretical semantics
• Probabilistic semantics are equivalent to a natural generalization of truth-value semantics. Like truth-value semantics, it is also non-referential in nature.

Semasiology is a discipline of linguistics concerned with the question "what does the word ♠️ mean?". It studies the meaning of words regardless how they are pronounced. It is the opposite of onomasiology, a branch of lexicology that starts with a concept or object and asks for its name, i.e., "how do you express ♠️?" whereas semasiology starts with a word and asks for its meanings. The exact meaning of semasiology is somewhat obscure. It is often used as a synonym for semantics (the study of the meaning of words, phrases, and longer forms of expression). However, semasiology is also sometimes considered part of lexical semantics, a narrow subfield of lexicology (the study of words) and semantics.

Situation semantics attempts to provide a solid theoretical foundation for reasoning about common-sense and real-world situations, typically in the context of theoretical linguistics, theoretical philosophy, or applied natural language processing.