Table of Contents
Overview
Definition of Semantics
Semantics is the study of meaning in languages, focusing on how linguistic meaning is constructed and arises. This field examines the interaction of meanings across various linguistic constituents, such as morphemes, words, clauses, sentences, and texts, and how these meanings relate to the minds of language users and their contexts.[4.1] [4.1] Linguistic semantics, a subfield, delves into how languages organize and express meanings, emphasizing grammar, language use, and acquisition.[3.1] It is descriptive, aiming to understand how meaning functions without prescribing specific meanings to expressions.[4.1] The study of semantics covers topics such as word meanings, sentence structures, and the interpretation of texts and discourse.[5.1] It intersects with disciplines like linguistics, semiotics, and philosophy, and includes theories such as truth-conditional semantics.[4.1] Overall, semantics is foundational to understanding language and communication.Key Concepts in Semantics
Semantics encompasses various key concepts that are essential for understanding meaning in language. Two primary branches of semantics are lexical semantics and compositional semantics. Lexical semantics focuses on the meanings of individual words, exploring the relationships between word meanings and the semantic features that differentiate similar words.[7.1] This branch is crucial for understanding how children learn to map sounds to meanings, as most sound-meaning pairs are arbitrary and require memorization.[8.1] In contrast, compositional semantics examines how the meanings of phrases and sentences are constructed from the meanings of individual words. This area is governed by the principle of compositionality, which posits that the meaning of a whole phrase can be derived from the meanings of its parts, taking into account the syntactic structure that conveys additional meaning.[11.1] Furthermore, semantics also intersects with pragmatics, which deals with the meaning of utterances in context, highlighting the importance of situational factors in interpreting language.[8.1] Understanding these distinctions between lexical and compositional semantics is vital for a comprehensive grasp of how meaning is constructed and interpreted in various linguistic contexts.[10.1]History
Evolution of Semantics in Linguistics
The evolution of semantics in linguistics has been significantly shaped by various scholars and their contributions over time. The study of meaning in language has roots in multiple disciplines, including logic, philosophy, and linguistics, with Richard Montague being a pivotal figure in its development during the late 1960s and early 1970s. Montague's work laid the foundation for formal semantics, integrating natural language semantics with formal logic, particularly through the use of intensional logic and lambda calculus, which bridged the gap between linguistics and mathematical logic.[56.1] Prior to Montague, early German semasiologists such as Christian Karl Reisig and Friedrich Haase made substantial contributions to the establishment of semantic theorizing as a legitimate discipline within linguistics. Reisig emphasized the need for a new branch of linguistics, semasiology, to explore the rules governing the development of word meanings, arguing that meaning could not be adequately addressed through etymology or syntax alone.[52.1] Haase, on the other hand, focused on the historical evolution of language and how the genius of individual nations is expressed through their linguistic changes, thereby complementing Reisig's logical approach with a historical perspective.[51.1] The dynamic nature of semantics is further illustrated by the work of Jost Trier in the 1930s, who introduced semantic field theory, positing that words are not isolated but rather organized into meaning-groups, thus opening new avenues for understanding semantic change.[52.1] This historical context highlights the gradual shift in focus from purely logical frameworks to more comprehensive approaches that consider the interplay of historical, cultural, and cognitive factors in the study of meaning. In contemporary contexts, the evolution of semantics has also been influenced by technological advancements, particularly in the digital age. The emergence of the Semantic Web and linked data has necessitated new semantic models to facilitate the organization and interpretation of information online, underscoring the ongoing relevance of semantic theories in modern applications.[49.1] Overall, the evolution of semantics reflects a rich interplay of ideas and methodologies that continue to shape our understanding of meaning in language.In this section:
Linguistic Semantics
Language Use and Acquisition
Linguistic semantics is essential for understanding language use and acquisition, focusing on how meaning is constructed and comprehended within cognitive processes. It involves the study of meaning in language, covering individual words, phrases, sentences, and larger discourse units. Semantics explores the relationship between words and their meanings, as well as how these meanings are constructed, understood, and communicated in specific contexts.[91.1] The interplay between language and thought is a key area of study in semantics and the philosophy of mind. Language is not just a communication tool; it is deeply connected to cognitive processes, shaping how individuals think, reason, and perceive the world.[89.1] Cognitive semantics suggests that linguistic knowledge is part of general cognition, indicating that the differences between language and other mental processes are more about degree than kind.[90.1] This view highlights that meaning construction in language understanding depends on the interaction between various types of knowledge representation, such as lexical concepts and cognitive models.[92.1] Advancements in linguistic semantics significantly impact the development of sophisticated natural language processing (NLP) models. As semantic theories progress, they guide the creation of models that better grasp context and nuance in human language. Recent NLP techniques, like multi-task learning and pre-training/fine-tuning regimens, have incorporated cognitive data to improve performance on language tasks.[95.1] These developments underscore the need for models that capture the complexities of human language, reflecting the cognitive mechanisms underlying language use.[94.1] Furthermore, the importance of semantics extends to practical applications, such as the Semantic Web, where linked data enhances the organization and retrieval of information online.[88.1] Integrating semantic principles into technology not only improves information access but also fosters innovation in how individuals and businesses interact with data. Thus, the study of linguistic semantics is foundational to understanding language and pivotal in shaping future advancements in language technology and cognitive science.In this section:
Semantics Vs. Pragmatics
Distinctions Between the Two Fields
Semantics and pragmatics are two distinct yet interrelated branches of linguistics that explore the meaning of language, each with its own focus and methodology. Semantics is primarily concerned with the literal meanings of words, phrases, and sentences, examining how language conveys specific meanings independent of context. It analyzes the structure and relationships of linguistic expressions, adhering to general rules of language that remain constant across different contexts [137.1]. In contrast, pragmatics emphasizes the role of context, intentions, and social factors in interpreting meaning. It investigates how meaning is constructed and understood in specific situations, taking into account the relationships between interlocutors and the broader communicative context [139.1]. One of the key distinctions between the two fields lies in their dependence on context. Semantics is context-independent, focusing solely on the inherent meanings of words and their interrelations, while pragmatics is context-dependent, considering how the meaning of a word or expression can change based on situational factors [138.1]. For instance, the phrase "Can you pass the salt?" semantically conveys a request for salt, but pragmatically, it may imply a social expectation or a polite request depending on the context in which it is used [136.1]. Furthermore, semantics has a narrower scope, dealing with meaning in a general sense, whereas pragmatics encompasses a broader range of factors, including speaker intentions and inferred meanings [139.1]. This distinction is crucial in fields such as English as a Foreign Language (EFL) instruction, where understanding both semantics and pragmatics is essential for effective communication and language proficiency development [140.1]. In summary, while both semantics and pragmatics study meaning in language, they approach it from different angles, with semantics focusing on literal meanings and pragmatics on contextual implications.Importance of Context in Meaning
The interplay between semantics and pragmatics is crucial in understanding how context shapes meaning in language. Semantics primarily focuses on the literal meanings of words and sentences, examining the structure and relationships of linguistic units. In contrast, pragmatics delves into the broader context, including speaker intentions and social factors, which significantly influence how language is interpreted [145.1]. Speaker intentions play a vital role in this dynamic, as they provide essential context that can alter the interpretation of words and phrases. For instance, the semantic meaning of the word "bank" typically refers to a financial institution; however, the pragmatic context can clarify which specific bank is being referenced based on the speaker's intent and the listener's assumptions [144.1]. This example illustrates how pragmatics can reveal deeper layers of meaning that semantics alone may not capture. Moreover, understanding language involves more than merely decoding its literal meanings. Pragmatic processes such as reference resolution, disambiguation, and implicature derivation are integral to effective communication. These processes help listeners infer meanings that are not explicitly stated, thereby enhancing comprehension [146.1]. For example, if someone says, "Sally ate some cookies," the pragmatic inference might suggest that she did not eat all the cookies, highlighting the importance of context in interpreting statements [146.1]. The distinction between semantic meaning and pragmatic inference becomes particularly evident in real-time language comprehension. Cognitive processes differentiate between these two aspects, allowing individuals to navigate ambiguities and derive meaning based on contextual cues [146.1]. This distinction underscores the necessity of considering both semantics and pragmatics to fully grasp the complexities of language use in social interactions.Recent Advancements
Impact on Artificial Intelligence
Recent advancements in semantics have significantly impacted artificial intelligence (AI), particularly in the realm of natural language processing (NLP) and the handling of sensitive data. As deep learning (DL) models for NLP tasks increasingly process private data, the necessity for robust privacy protections has become paramount. This is underscored by data protection regulations such as the European Union's General Data Protection Regulation (GDPR), which mandates stringent privacy measures in AI applications.[180.1] To address these privacy concerns, various solutions have been proposed that integrate existing data-privacy techniques, including differential privacy and modern cryptographic methods. For instance, homomorphic encryption is one of the techniques reviewed for its advantages and challenges when applied to deep learning models.[179.1] However, implementing differential privacy within deep learning frameworks presents unique challenges due to the complexity and depth of neural networks, necessitating advanced methods to effectively manage these issues.[181.1] Moreover, a federated learning approach has been suggested as a means to enhance privacy and security by preventing the transfer or relocation of sensitive data off the network edge. This method has demonstrated superior performance in attack detection accuracy compared to traditional centrally managed systems.[178.1] As the volume of sensitive data recorded by online services continues to rise, the development of optimal private deep neural networks becomes increasingly critical, highlighting the ongoing challenges in balancing semantic extraction capabilities with user privacy.[182.1]Developments in Semantic Communication
Recent advancements in semantic communication have emerged as a significant area of research, particularly influenced by the integration of machine learning techniques. The concept of semantic communication, originally introduced by Weaver in 1949, has gained renewed attention due to its focus on the precise retrieval of meaning rather than merely the accurate transmission of data.[169.1] This shift emphasizes the importance of conveying relevant information tailored to the receiver's needs, thereby enhancing communication efficiency and context-awareness.[170.1] Recent studies have highlighted the integration of deep learning-based semantic extraction within communication architectures. This approach allows for the transmission of only the information of interest, alleviating bandwidth pressure and enhancing privacy by reducing redundant data exchanges.[167.1] Furthermore, a systematic review of advancements in semantic communications has identified key challenges and potential research directions, aiming to foster further scientific and industrial progress in this field.[168.1] The development of a unified framework for semantic communication, particularly in the context of Seb theory and Seb-based semantic transmission, has been recognized as a critical area for future exploration.[167.1] This framework is essential for the evolution of "Intellicise" wireless networks, which aim to optimize data transmission through intelligent processing methods.[171.1] Additionally, the integration of functional modules into semantic communications has been proposed, although challenges remain regarding the constraints of limited computational resources at the physical level.[173.1]In this section:
Applications Of Semantics
Role in Computer Science
Semantics plays a crucial role in various domains of computer science, particularly in natural language processing (NLP) and information retrieval. One of the foundational figures in formal semantics, Gottlob Frege, emphasized the importance of function-argument structure, which is essential for understanding semantic compositionality in computational contexts.[210.1] Richard Montague further advanced the field by developing formal systems that have influenced modern computational semantics, which automates the construction and reasoning of meaning representations for natural language expressions.[213.1] The evolution of semantics has significantly impacted the digital landscape, particularly through the development of semantic technologies that enhance user interaction and information retrieval. For instance, the emergence of semantic HTML has improved website accessibility for individuals with disabilities, allowing assistive technologies like screen readers to interpret content more effectively.[215.1] This not only enhances user experience but also boosts search engine optimization (SEO) by enabling search engines to better understand the context and relevance of web content.[215.1] Moreover, the shift from traditional keyword-based search methods to semantic search represents a significant advancement in information retrieval. Semantic search focuses on understanding the meanings behind queries rather than relying solely on literal string matches, thereby providing more accurate and context-aware results.[221.1] This approach addresses the limitations of keyword-based models, which often fail to capture the relationships between terms and their meanings.[220.1] By leveraging domain ontologies and knowledge bases, semantic search enhances the retrieval of information from large, unstructured data repositories.[219.1] In the realm of NLP, semantics is pivotal for deciphering the complexities of human language, enabling machines to grasp the nuances and meanings behind words.[232.1] As technologies like machine learning continue to evolve, the role of semantics is expected to expand, further influencing how information is retrieved and interacted with online. This evolution underscores the necessity for advanced semantic models capable of understanding natural language in all its complexity and variability.[217.1]Influence on Philosophy and Cognitive Psychology
The study of semantics has significantly influenced both philosophy and cognitive psychology, particularly in understanding meaning and its implications for human cognition and communication. Semantics, as a field, investigates how linguistic meaning arises and how it is constructed from various constituents, such as morphemes, words, and sentences, thereby providing insights into the cognitive processes underlying language use.[207.1] This inquiry into meaning is not merely theoretical; it has practical implications for how individuals think and communicate, shaping their interactions with the world.[225.1] In philosophy, the evolution of semantic theories has prompted critical discussions about the nature of meaning and reference. The contributions of scholars like Ogden and Richards, who combined philosophical and linguistic methodologies, highlight the dynamic nature of semantics and its relevance to philosophical inquiries about language.[206.1] The exploration of semantic shifts and variations across cultures further enriches philosophical debates, as it reveals how meaning can be context-dependent and influenced by cultural factors.[226.1] This understanding is crucial for addressing questions about how language reflects and shapes human thought. Cognitive psychology also benefits from advancements in semantics, particularly in the realm of language acquisition and comprehension. Research indicates that semantics plays a vital role in helping learners grasp the meanings of words and phrases, which is essential for effective communication.[224.1] Moreover, the application of semantic reasoning in educational contexts has been shown to enhance critical thinking and analytical skills among students, particularly in language learning environments.[228.1] By integrating semantic principles into language instruction, educators can foster a more engaging and effective learning experience that promotes deeper cognitive engagement.In this section:
Computer ScienceInformation RetrievalFormal SystemsComputational SemanticsSemantic Technologies
Challenges In Semantics
Current Research Gaps
Current research in semantic communications reveals several significant gaps that need to be addressed to enhance the effectiveness and applicability of this emerging field. One of the primary challenges is the integration of deep learning-based semantic extraction into communication architectures. While this integration can alleviate bandwidth pressure by transmitting only relevant information rather than raw data, there remains a lack of a systematic survey that provides a unified framework for semantic communication, particularly in the context of semantic information theory (SIT) and Seb-based semantic transmission frameworks.[281.1] Moreover, the balance between semantic accuracy and data compression efficiency presents another critical research gap. Achieving this balance is essential for practical applications, yet it poses challenges in terms of maintaining the integrity of transmitted information while optimizing bandwidth usage.[268.1] Researchers have noted that semantic communication can significantly enhance source data compression and support intelligent services, which are vital for the development of future sixth-generation (6G) networks.[283.1] However, the specific metrics and methodologies for evaluating these improvements are still under exploration, indicating a need for further investigation into goal-oriented semantic communications.[281.1] Additionally, the exploration of potential applications for semantic communication technology, such as in intelligent networks and the metaverse, highlights the necessity for more comprehensive research into the practical implementations of these solutions. Current literature suggests that while semantic communication has the potential to improve data transmission efficiency, the challenges encountered during implementation remain inadequately addressed.[282.1] Thus, there is a pressing need for ongoing research to identify and overcome these barriers, ensuring that semantic communication can fulfill its promise in future communication systems.Future Directions in Semantic Studies
The integration of semantic communication (SC) into future network infrastructures, particularly in the context of Sixth Generation (6G) networks, presents both opportunities and challenges. SC is recognized for its ability to incorporate application requirements and the meaning of information into data transmission processes, which is essential for enhancing communication efficiency in increasingly complex environments.[250.1] However, achieving secure and effective semantic communication remains a significant challenge due to the intricacies involved in ensuring that only relevant information is transmitted, thereby alleviating bandwidth pressure and enhancing privacy.[253.1] Recent advancements in artificial intelligence (AI) and computing power have opened avenues for developing communication systems capable of processing semantic information. These technologies can facilitate the integration of deep learning-based semantic extraction into communication architectures, allowing for the transmission of only the information of interest to the receiver.[259.1] This shift from raw data transmission to a more nuanced understanding of semantic messages is crucial for the evolution of intelligent communication systems, particularly as they support human-machine interactions and applications in the metaverse.[262.1] Despite these advancements, several technical challenges persist. The need for a unified framework that encompasses various aspects of semantic communication, including Seb-based semantic transmission and the development of intelligent networks, is still unmet.[260.1] Furthermore, the balance between semantic accuracy and data compression efficiency poses additional difficulties. Effective data compression is essential for optimizing energy efficiency, accuracy, and latency in sensor networks, yet it often compromises the accuracy of inferences derived from the transmitted data.[265.1] As the demand for intelligent services grows, the architecture of 6G networks is expected to evolve from a focus solely on high transmission rates to one that emphasizes intelligent connections among devices.[269.1] This transition underscores the importance of addressing the open issues and key challenges in semantic communication, which will be critical for promoting further research and development in this field.[261.1] Future studies must explore these challenges comprehensively to harness the full potential of semantic communication in next-generation networks.References
[3] Semantics: Definitions and Examples - ThoughtCo — Semantics: Definitions and Examples Linguistic semantics has been defined as the study of how languages organize and express meanings. Linguistic Semantics and Grammar Linguistic semantics looks not only at grammar and meaning but at language use and language acquisition as a whole. "The technical term for the study of meaning in language is semantics. "The job of semantics is to study the basic, literal meanings of words as considered principally as parts of a language system, whereas pragmatics concentrates on the ways in which these basic meanings are used in practice, including such topics as the ways in which different expressions are assigned referents in different contexts, and the differing (ironic, metaphorical, etc.) uses to which language is put,"
[4] Semantics - Wikipedia — Semantics is the study of meaning in languages. It is a systematic inquiry that examines what linguistic meaning is and how it arises. It investigates how expressions are built up from different layers of constituents, like morphemes, words, clauses, sentences, and texts, and how the meanings of the constituents affect one another. Semantics can focus on a specific language, like English, but in its widest sense, it investigates meaning structures relevant to all languages.[a][b] As a descriptive discipline, it aims to determine how meaning works without prescribing what meaning people should associate with particular expressions. Some of its key questions are "How do the meanings of words combine to create the meanings of sentences?", "How do meanings relate to the minds of language users, and to the things words refer to?", and "What is the connection between what a word means, and the contexts in which it is used?". The main disciplines engaged in semantics are linguistics, semiotics, and philosophy. Besides its meaning as a field of inquiry, semantics can also refer to theories within this field, like truth-conditional semantics, and to the meaning of particular expressions, like the semantics of the word fairy.
[5] (PDF) An introduction to semantics - Academia.edu — This booklet provides an introduction to the field of semantics and aims to give university students a brief summary of the main concepts and theories. Semantics is the study of meaning in language and encompasses a wide range of topics, from word meanings and sentence structures to the interpretation of texts and discourse.
[7] Lexical semantics and compositional semantics | Semantic Technologies — It is concerned with individual words (unlike compositional semantics, which is concerned with meanings of sentences.) Of the many ways that lexical semantics can be studied, we'll look in general terms at the meaning relationships that word meanings have with one another and the semantic features that help to differentiate similar words.
[8] Semantics Study Guide - Semantics SG Lexical vs. Compositional Lexical ... — Semantics SG. Lexical vs. Compositional Lexical - meaning of individual words To learn lexicon, kids must map sounds to meanings and most sound-meaning pairs are arbitrary and just memorized Compositional - meaning of phrases and sentences Pragmatics - meaning of an utterance in context Over extension - assigning too many objects to one
[10] Lexical vs. Semantic - What's the Difference? - This vs. That — Semantics: Further Detail. Introduction. Lexical and semantic attributes are two important concepts in the field of linguistics that play a crucial role in understanding language. While they are closely related, they have distinct characteristics that set them apart. ... One key difference between lexical and semantic attributes is their level
[11] PDF — Compositional and lexical semantics Compositional semantics: the construction of meaning (generally expressed as logic) based on syntax. This lecture: Œ Semantics with FS grammars Lexical semantics: the meaning of individual words. This lecture: Œ lexical semantic relations and WordNet Œ one technique for word sense disambiguation 1
[49] The Evolution of Semantics: A History From Linguistic Roots to AI ... — By examining the evolution of semantics, we can better understand its role in shaping the digital age and its future potential in AI and data science. Hypertext and Navigation: The Web’s hyperlink structure created new ways of organizing and accessing information, requiring new semantic models to understand how users navigate and interpret content. These developments underscored the need for more advanced semantic technologies capable of understanding natural language in all its complexity and variability. Machine-Readable Data: Embedding semantic information directly into web pages using standardized formats. Linked Data, a method of publishing structured data so that it can be interlinked and become more useful, emerged as a practical application of Semantic Web principles. Semantic Search: Search engines can use Linked Data to provide more accurate and context-aware results.
[51] The theory of word formation in early semasiology: A blank spot on the ... — Abstract This paper brings to light the contribution the early German semasiologists - most prominently Christian Karl Reisig, Friedrich Haase, and, later, Ferdinand Heerdegen - made to the establishment of semantic theorising as a genuine discipline in linguistics in general and, specifically, in grammaticography.
[52] PDF — He came to the conclusion that the study of meaning cannot be successfully dealt with either within etymology, or syntax and that is why a new branch of linguistics – semasiology – was needed, whose task would be to discover rules governing the development of word meaning.2 The objective of Reisig (1890:1–2) was to focus on semantic change as a major area of linguistic interest, and to show the unfolding of the train of thought with regard to the meaning of the words and to provide a derivation of all subsequent meanings from the first in a logical and historical order.3 It needs to be mentioned that the quest to reveal semantic laws was prompted by a series of successes in phonetics and historical comparative philology in general. Field theory approach In the 1930s Jost Trier (1894–1970) published a series of articles on semantic field theory which opened a new phase in the study of semantic change.23 He claimed that: […] individual words in a language do not stand alone but are arranged in meaning-groups.
[56] PDF — formal semantics over the last 50 years is a story of collaboration among linguists, logicians, and philosophers. One central figure was the logician and philosopher Richard Montague, a student of Tarski's, whose seminal works on the formal semantics of natural language date from the late 1960's and early 1970's.
[88] A Brief History of Semantics - DATAVERSITY — It uses the “hidden,” encoded data and, more recently, natural language processes to search for, compile, and organize information from the web. The concept of linked data has been a very useful aspect of the Semantic Web, and is remarkably functional as an education tool. Virtual assistants and services are beginning to exchange useful information all over the Semantic Web. Virtual assistants, such as Google Now and Siri, have initiated a wide range of start-ups, especially those providing automated services. The merging of trends in technology and the business world is creating a new cycle of innovation affecting the way individuals and businesses perform their work, and even how data is collected into useful information. The use of semantics provides a virtual assistant capable of working independently and processing significant amounts of data.
[89] Western Perspectives on the Interconnection of Mind and Language — The relationship between mind and language is one of the most fascinating and complex topics in Western philosophy. Language is not just a tool for communication—it is deeply intertwined with the way we think, reason, and experience the world.
[90] PDF — From the outlined relationships between thought and reality and thought and language follows the role cognitive semantics assigns to language. In this view, "linguistic knowledge is part of general cognition" which means that the difference between language and other mental processes is only one of degree and not one of kind (Saeed, 1997
[91] 18 Semantics Examples (2025) - Helpful Professor — Definition of Semantics. In linguistics, semantics is defined as the study of meaning in language, including the study of meaning in individual words, phrases, sentences, and larger discourse units (Riemer, 2010).. It is concerned with examining the relationship between words and their meanings and how these meanings are constructed, understood, and communicated within particular contexts.
[92] How Words Mean: Lexical Concepts, Cognitive Models, and Meaning ... — The essential insight of the theory is that meaning construction in language understanding relies upon the interaction between distinct types of knowledge representation — units of semantic structure: lexical concepts, and units of conceptual structure: cognitive models — which inhere in distinct representational systems that evolved for
[94] Semantics and Semantic Interpretation - Principles of Natural Language ... — This chapter will consider how to capture the meanings that words and structures express, which is called semantics. The goal of a meaning representation is to provide a mapping between expressions of language to concepts in some computational model of a domain, which might be specified as a software application or as a set of well-formed formulas in a logic (or as some hybrid of the two, such
[95] Editorial: Perspectives for natural language processing between AI ... — Some recent activities and findings in this direction include: Techniques like multi-task learning have been used to integrate cognitive data as supervision in NLP tasks (Barrett et al., 2016); Pre-training/fine-tuning regimens are potentially interpretable in terms of cognitive mechanisms like general competencies applied to specific tasks (Flesch et al., 2018); The ability of modern models for ‘few-shot' or even ‘zero-shot' performance on novel tasks mirrors human performance (Srivastava et al., 2018); Evidence of unsupervised structure learning in current neural network architectures that mirrors classical linguistic structures (Hewitt and Manning, 2019; Tenney et al., 2019). The current computational paradigms can offer new ways to explore human language learning and processing, while linguistic and cognitive research can highlight those aspects of human intelligence that systems need to model or incorporate within their architectures.
[136] Pragmatics vs. Semantics - What's the Difference ... - This vs. That — While semantics focuses on the literal meaning of words and sentences, pragmatics delves into the broader context and intentions behind the use of language. While pragmatics and semantics both focus on the study of meaning in language, they approach it from different angles. Pragmatics emphasizes the role of context, intentions, and social factors in interpreting meaning, while semantics focuses on the structure and relationships of words, phrases, and sentences. In conclusion, pragmatics and semantics are two important branches of linguistics that explore the meaning of language. Pragmatics focuses on the role of context, intentions, and social factors in interpreting meaning, while semantics examines the structure and relationships of words, phrases, and sentences.
[137] Semantics vs. Pragmatics — What's the Difference? — Semantics is a branch of linguistics that delves into the meanings of words, phrases, and sentences. It seeks to understand how language conveys specific meanings, irrespective of context. On the other hand, Pragmatics is more concerned with how people use language in different situations and contexts.
[138] Semantics and Pragmatics: What Is the Difference? — So semantics is context independent while pragmatics is context dependent. Semantics has a narrower scope as it only deals with meaning in a general sense, using the general rules used in a language. The meaning of a word or expression and their relation to one another remains constant.
[139] Semantics and Pragmatics: What Is the Difference? — Learn how semantics and pragmatics, two branches of linguistics, study the meaning of words, phrases, and sentences in different ways. Semantics focuses on literal and grammatical meaning, while pragmatics considers context, intention, and inference.
[140] Enhancing Semantic and Pragmatic Instruction in EFL Higher ... - EL21C — In English as a Foreign Language (EFL) instruction, semantics and pragmatics play fundamental roles in facilitating effective communication and language proficiency development. Semantics enhances students' comprehension and recognition of word definitions, connections between sentences, as well as discourse and contextual understanding (Alsayed, 2019). Pragmatics is also essential for EFL
[144] Semantics and Pragmatics: The Dynamic Relationship Between Meaning and ... — For example, in the sentence “i am going to the bank,” the word “bank” may have a semantic meaning of a financial institution, but the pragmatics of the sentence could reveal which specific bank the speaker is referring to, based on the context and the listener’s assumption. While semantics focuses on the meaning of individual words and how they combine to form sentences, pragmatics looks at how context affects the interpretation of meaning and the use of language in social situations. In language communication, semantics plays a crucial role in enabling us to convey meaning accurately, while pragmatics considers the context in which language is used and how social and cultural factors influence language use.
[145] Pragmatics vs. Semantics - What's the Difference? - This vs. That — While semantics focuses on the literal meaning of words and sentences, pragmatics delves into the broader context and intentions behind the use of language. While pragmatics and semantics both focus on the study of meaning in language, they approach it from different angles. Pragmatics emphasizes the role of context, intentions, and social factors in interpreting meaning, while semantics focuses on the structure and relationships of words, phrases, and sentences. In conclusion, pragmatics and semantics are two important branches of linguistics that explore the meaning of language. Pragmatics focuses on the role of context, intentions, and social factors in interpreting meaning, while semantics examines the structure and relationships of words, phrases, and sentences.
[146] Perceptual, Semantic, and Pragmatic Factors Affect the Derivation of ... — Understanding language requires more than decoding its literal meaning: pragmatic processes such as reference resolution (e.g., inferring who 'John' is in an utterance), disambiguation (e.g., identifying the intended meaning of 'bank') or implicature derivation (e.g., inferring that Sally did not eat all the cookies, if she ate 'some cookies') are pervasive in language comprehension.
[167] Advances and challenges in semantic communications: A systematic review ... — Advances and challenges in semantic communications: A systematic review | National Science Open (NSO) Based on this, in semantic communication systems, deep learning-based semantic extraction can be integrated into the communication architecture, which allows only the information of interest to the receiver for transmission, rather than raw data, thereby alleviating bandwidth pressure and enhancing privacy preservation by reducing the redundant data to be exchanged. There is still a lack of a systematic survey article that provides a unified framework of semantic communication in the area of Seb theory, Seb-based semantic transmission, and semantic communication empowered “Intellicise” wireless network. (1) We discuss the recent advancements in investigating SIT and developing Seb-based semantic communication architecture for wireless networks. Towards goal-oriented semantic communications: New metrics, open challenges, and future research directions.
[168] Advances and challenges in semantic communications: A systematic review — Their article summarizes the advances made in semantic information and semantic communications. It also discusses the main challenges, key issues, and potential research directions in developing modern semantic communication, aiming to prompt further scientific and industrial advances in semantic communications. ... New research finds two
[169] Advances and challenges in semantic communications: A ... - SciEngine — Inspired by the recent success of machine learning (ML), the concept of semantic communication introduced by Weaver in 1949 has gained significant attention and has become a promising research direction. Unlike conventional communication systems, semantic communication emphasizes the precise retrieval of conveyed meaning from the source to the receiver, rather than focusing on the accurate
[170] Semantic Communication on Digital Wireless Communication Systems - MDPI — Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of physical layer processing modules in end
[171] Advances and challenges in semantic communications: A systematic review ... — Advances and challenges in semantic communications: A systematic review | National Science Open (NSO) Based on this, in semantic communication systems, deep learning-based semantic extraction can be integrated into the communication architecture, which allows only the information of interest to the receiver for transmission, rather than raw data, thereby alleviating bandwidth pressure and enhancing privacy preservation by reducing the redundant data to be exchanged. There is still a lack of a systematic survey article that provides a unified framework of semantic communication in the area of Seb theory, Seb-based semantic transmission, and semantic communication empowered “Intellicise” wireless network. (1) We discuss the recent advancements in investigating SIT and developing Seb-based semantic communication architecture for wireless networks. Towards goal-oriented semantic communications: New metrics, open challenges, and future research directions.
[173] Semantic Communications using Foundation Models: Design Approaches and ... — Current research , guides the integration of FMs into semantic communications. However, there is a lack of discussion on leveraging FMs across multiple dimensions of semantic communications, especially considering the con-straints of limited computational resources at the physical level. This article begins by outlining the current semantic
[178] A comprehensive review on deep learning algorithms: Security and ... — Proposed federated learning approach addresses privacy and security concerns about data privacy and security rather than allowing data to be transferred or relocated off the network edge: Solution outperforms a standard centrally managed system in terms of attack detection accuracy, according to our comparative performance analysis.
[179] [1807.11655] Security and Privacy Issues in Deep Learning - arXiv.org — To maintain data privacy, several solutions that combine existing data-privacy techniques have been proposed, including differential privacy and modern cryptography techniques. In this paper, we describe the notions of some of methods, e.g., homomorphic encryption, and review their advantages and challenges when implemented in deep-learning models.
[180] How to keep text private? A systematic review of deep learning methods ... — Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures. Data protection laws, such as the European Union's General Data Protection Regulation (GDPR), thereby enforce the need for privacy. Although many privacy-preserving NLP methods have been proposed in recent years, no categories to organize
[181] Differential Privacy: Balancing Data Utility and User Privacy in ... — Implementing differential privacy in deep learning involves unique challenges due to the complexity and depth of neural networks. Let's explore two advanced methods that address these challenges.
[182] Optimal Balance of Privacy and Utility with Differential Privacy Deep ... — As the number of online services has increased, the amount of sensitive data being recorded is rising. Simultaneously, the decision-making process has improved by using the vast amounts of data, where machine learning has transformed entire industries. This paper addresses the development of optimal private deep neural networks and discusses the challenges associated with this task. We focus
[206] Brief History of Semantics - Academia.edu — Additionally, the roles of Odgen and Richards in combining philosophical and linguistic methodologies are examined, alongside the dynamic nature of semantics as evidenced by changes in word meanings over time. Semantics is the study of meaning in language. Semantics is that level of linguistic analysis where meaning is analyzed. This article highlights briefly the issues related to the notions like meaning and semantics and at the same time looks through the branches of semantics providing linguistic scholars views in this field 6 SEMANTICS The Study of Meaning Semantics: Word and sentence meaning SEMANTICS Semantics is the study of linguistic meaning of morphemes
[207] Semantics - Wikipedia — Semantics is the study of meaning in languages. It is a systematic inquiry that examines what linguistic meaning is and how it arises. It investigates how expressions are built up from different layers of constituents, like morphemes, words, clauses, sentences, and texts, and how the meanings of the constituents affect one another. Semantics can focus on a specific language, like English, but in its widest sense, it investigates meaning structures relevant to all languages.[a][b] As a descriptive discipline, it aims to determine how meaning works without prescribing what meaning people should associate with particular expressions. Some of its key questions are "How do the meanings of words combine to create the meanings of sentences?", "How do meanings relate to the minds of language users, and to the things words refer to?", and "What is the connection between what a word means, and the contexts in which it is used?". The main disciplines engaged in semantics are linguistics, semiotics, and philosophy. Besides its meaning as a field of inquiry, semantics can also refer to theories within this field, like truth-conditional semantics, and to the meaning of particular expressions, like the semantics of the word fairy.
[210] PDF — The greatest foundational figure for formal semantics is Gottlob Frege (1848-1925). He is credited with a number of ideas that have been crucial for logic and for semantics. One of his central contributions is the idea that function-argument structure is the key to semantic compositionality. Without the idea that some expressions denote
[213] Computational semantics - Wikipedia — Computational semantics is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions. It consequently plays an important role in natural-language processing and computational linguistics.. Some traditional topics of interest are: construction of meaning representations, semantic underspecification, anaphora
[215] The Importance of Semantic HTML for SEO and Accessibility — This improves website accessibility for people with disabilities and boosts SEO performance, as search engines can better understand the context and relevance of the content. Semantic HTML results in cleaner, more maintainable code and fosters a more inclusive, effective web experience for all users.
[217] The Evolution of Semantics: A History From Linguistic Roots to AI ... — By examining the evolution of semantics, we can better understand its role in shaping the digital age and its future potential in AI and data science. Hypertext and Navigation: The Web’s hyperlink structure created new ways of organizing and accessing information, requiring new semantic models to understand how users navigate and interpret content. These developments underscored the need for more advanced semantic technologies capable of understanding natural language in all its complexity and variability. Machine-Readable Data: Embedding semantic information directly into web pages using standardized formats. Linked Data, a method of publishing structured data so that it can be interlinked and become more useful, emerged as a practical application of Semantic Web principles. Semantic Search: Search engines can use Linked Data to provide more accurate and context-aware results.
[219] Semantically enhanced Information Retrieval: An ontology-based approach — Aiming to solve the limitations of keyword-based models, the idea of semantic search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the Information Retrieval (IR) and the Semantic Web (SW) communities. In order to address the shortcomings in prior semantic search approaches, this work proposes the exploitation of fine-grained domain ontologies and KBs to improve semantic retrieval in large repositories of unstructured information, extending the general ontology-based search capabilities towards more widely applicable IR-oriented search capabilities.
[220] Semantic and keyword based web techniques in information retrieval ... — Semantic and keyword web based technique is becoming a generic issue in an application of Information Retrieval (IR). Most of the researchers used different web techniques for finding relevant information and find the keyword based search, which are not able to fetch the relevant search result because they do not know the actual meaning of the term or expression and relationship between them
[221] Semantic Search Vs Information Retrieval - Restackio — Conclusion Semantic search represents a significant advancement in the field of information retrieval, offering a more nuanced and effective way to connect users with the information they seek. By understanding the differences between semantic and lexical search, users can better leverage these technologies to enhance their search experiences.
[224] The Role of Semantics in Language Acquisition: Understanding Word Meanings — Another area of interest is the study of how semantics differs across languages, and how this affects language acquisition and communication. Semantics plays a crucial role in language acquisition, as it helps learners understand the meanings of words and phrases, and how these words are used in different contexts.
[225] How Language Shapes Thought: The Everyday Impact of Meaning — Semantics, the study of meaning in language, is not just an abstract academic subject. It influences each interaction we have, shaping how we think, communicate, and navigate the world.
[226] Exploring Cultural Differences in Language Meaning: The ... - TCL Lab — Exploring Cultural Differences in Language Meaning: The Intriguing World of Semantics – TCL Lab Exploring Cultural Differences in Language Meaning: The Intriguing World of Semantics Exploring Cultural Differences in Language Meaning: The Intriguing World of Semantics Understanding semantic variations across different cultures is essential, especially in a globalised world. In some languages, the same word can have different meanings depending on the context in which it is used. Exploring The Differences In Semantic Meanings Across Languages One of the most striking differences in semantic meanings across languages is the way in which tense and aspect are expressed. In a globalised world, understanding semantic variations across different cultures is crucial for effective communication.
[228] PDF — thinking skills. This paper investigates why and how critical thinking is useful in language study, and how it can be honed to make language acquisition more fruitful. Critical thinking is essential for language acquisition. Significant research emphasizes the importance of critical thinking in language learning, particularly for enhancing
[232] The Impact Of Semantics On Machine Learning: Unlocking The Power Of ... — The role of semantics in Natural Language Processing. In the realm of Natural Language Processing (NLP), semantics plays a pivotal role in deciphering the complexities of human language. It's the key to unlocking the true meaning behind the words, allowing machines to comprehend the nuances of language and make sense of the world around us.
[250] Semantic Communication Empowered 6G Networks: Techniques, Applications ... — Semantic communication (SC) is a promising solution for future 6G networks due to its natural capability of integrating application requirements and information meaning into data transmission processes. In this paper, a comprehensive survey that overviews how SC can be applied for 6G networks and the key technologies of SC is presented.
[253] Secure Semantic Communications: Challenges, Approaches, and ... — Semantic communication has emerged as a promising technique that can integrate the meaning and context of information into a communication process. It goes beyond raw data transmission and enables a sender and a receiver to transmit and recover semantic messages through their respective knowledge bases. However, it is challenging to achieve secure semantic communication due to complicated
[259] PDF — With the advancements in artificial intelligence (AI) and computing power technology, there is now an opportunity to develop communication systems that can process semantic information. Recent advancements in AI have shown great potential in wireless communication scenarios, providing a viable path for under-
[260] Advances and challenges in semantic communications: A systematic review ... — Advances and challenges in semantic communications: A systematic review | National Science Open (NSO) Based on this, in semantic communication systems, deep learning-based semantic extraction can be integrated into the communication architecture, which allows only the information of interest to the receiver for transmission, rather than raw data, thereby alleviating bandwidth pressure and enhancing privacy preservation by reducing the redundant data to be exchanged. There is still a lack of a systematic survey article that provides a unified framework of semantic communication in the area of Seb theory, Seb-based semantic transmission, and semantic communication empowered “Intellicise” wireless network. (1) We discuss the recent advancements in investigating SIT and developing Seb-based semantic communication architecture for wireless networks. Towards goal-oriented semantic communications: New metrics, open challenges, and future research directions.
[261] PDF — This article conducts a comprehensive survey of modern semantic communication theories and methods and provides an in-depth introduction to modern semantic communication research, including the semantic base (Seb)-based semantic transmission framework and semantic communication-empowered intelligent and concise (Intellicise) networks. Credit: Science China Press Then, this review explores the potential applications of modern semantic communication technology, including Intellicise networks, goal-oriented applications, and the metaverse. Finally, this review elaborates on open issues and key challenges in semantic communication and explores potential research directions to promote further research on modern semantic communication theories and methods. More information: Ping Zhang et al, Advances and Challenges in Semantic Communications: A Systematic Review, National Science Open (2023).
[262] A survey on semantic communications: Technologies, solutions ... — As Sixth Generation (6G) networks enter the era of intelligent communications, it gives rise to Semantic Communication (SC) technology that widely supports human-machine interaction, intelligent communication, virtual human interaction, and metaverse, breaking through the traditional Shannon paradigm. Recently, many researchers have turned to SC, a promising communication scheme for the
[265] PDF — Historically, data compression has been used to address the first three of these problems: minimizing the impact on accuracy of reducing a data volume that is ill-supported by the communication capacity. However, as we discuss in Section 2, it is also possible to use data compression to attack the energy issues.
[268] PDF — receiver, rather than focusing on the accurate transmission of symbols. Thus, semantic communication can achieve a significant gain in source data compression, alleviate communication bandwidth pressure, and support new intelligent services, which is envisioned as a crucial enabler of future sixth-generation (6G) networks.
[269] Semantic Communications for Future Internet: Fundamentals, Applications ... — With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on a high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and the
[281] Advances and challenges in semantic communications: A systematic review ... — Advances and challenges in semantic communications: A systematic review | National Science Open (NSO) Based on this, in semantic communication systems, deep learning-based semantic extraction can be integrated into the communication architecture, which allows only the information of interest to the receiver for transmission, rather than raw data, thereby alleviating bandwidth pressure and enhancing privacy preservation by reducing the redundant data to be exchanged. There is still a lack of a systematic survey article that provides a unified framework of semantic communication in the area of Seb theory, Seb-based semantic transmission, and semantic communication empowered “Intellicise” wireless network. (1) We discuss the recent advancements in investigating SIT and developing Seb-based semantic communication architecture for wireless networks. Towards goal-oriented semantic communications: New metrics, open challenges, and future research directions.
[282] Advances and challenges in semantic communications: A systematic review ... — Advances and challenges in semantic communications: A systematic review | National Science Open (NSO) Advances and challenges in semantic communications: A systematic review Thus, semantic communication can achieve a significant gain in source data compression, alleviate communication bandwidth pressure, and support new intelligent services, which is envisioned as a crucial enabler of future sixth-generation (6G) networks. In this review, we critically summarize the advances made in semantic information and semantic communications, including theory, architecture, and potential applications. Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform. Next article Show article metrics EDP Sciences is a long-established open access publisher.
[283] A survey on semantic communications: Technologies, solutions ... — As Sixth Generation (6G) networks enter the era of intelligent communications, it gives rise to Semantic Communication (SC) technology that widely supports human-machine interaction, intelligent communication, virtual human interaction, and metaverse, breaking through the traditional Shannon paradigm. Recently, many researchers have turned to SC, a promising communication scheme for the