INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 72
WHAT IS SEMANTIC ANALYSIS IN MODERN LINGUISTICS?
Razoqova Kumush Sadriddinovna
BSU, 1
st
year doctoral student
Abstract:
Semantic analysis is a fundamental aspect of contemporary linguistics that is
concerned with the study of meaning in language. It examines how meaning is expressed
through words, phrases, sentences, and larger units of discourse and how the context of use
influences interpretation. The article covers the theoretical foundations of semantic analysis,
its methodology, and its applications to natural language processing (NLP), computational
linguistics, and cognitive science. Semantic analysis enables linguists and researchers to
better comprehend the subtleties of human communication and create advanced language
technologies.
1. Introduction
Semantic analysis is an area of linguistics concerned with the study of meaning in language.
It investigates how meaning is constructed, interpreted, and communicated by linguistic
expressions. Semantic analysis takes center stage in modern linguistics in studying the
interdependence between cognition and language, and in designing technologies to process
and generate human language.
The role of semantic analysis has grown with the development of computational linguistics
and artificial intelligence (AI). It is now a cornerstone of natural language processing (NLP),
enabling machines to understand, interpret, and generate human language more and more
precisely.
2. Theoretical Foundations of Semantic Analysis
Semantic analysis has its origins in many linguistic theories and frameworks, including:
2.1. Structural Semantics
Structural semantics is concerned with relations between words and how their meaning is
defined in relation to each other. Key concepts are:
- Lexical relations: Synonymy, antonymy, hyponymy, and meronymy.
- Semantic fields: Groups of words having a shared theme or domain.
2.2. Formal Semantics
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 73
Formal semantics uses mathematical and logical systems to represent meaning. It is interested
in:
- Truth-conditional semantics: The truth conditions of a sentence constitute its meaning.
- Compositionality: A compound expression's meaning is decided on the basis of its
constituents' meaning and their syntactic structure.
2.3. Cognitive Semantics
Cognitive semantics examines the interaction between thought and language. It is concerned
with:
- Conceptual metaphors: Abstract concepts are described in relation to concrete experience.
- Prototype theory: Categories are organized around prototype examples rather than
boundaries.
2.4. Pragmatics
While not strictly a semantics subfield, pragmatics fills out semantic analysis by examining
how context makes meaning. Key concepts are:
- Speech acts: Actions performed through the use of language (requests, promises, etc.).
- Implicature: Implicated but not explicitly stated meanings.
3. Methodologies in Semantic Analysis
Modern semantic analysis employs a variety of methodologies, both theoretical and
computational:
3.1. Corpus Linguistics
Corpus linguistics involves examining large sets of texts to identify patterns of meaning.
Techniques include:
- Collocation analysis: Determining which words co-occur.
- Concordance analysis: Examining the context in which particular words appear.
3.2. Computational Semantics
Computational semantics uses algorithms and models to process and generate meaning. Key
strategies include:
- Word embeddings: Representing words as vectors in a high-dimensional space (e.g.,
Word2Vec, GloVe).
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 74
- Semantic role labeling: Identifying the function of words within a sentence (e.g., agent,
patient).
3.3. Experimental Semantics
Experimental semantics uses psycholinguistic experiments to study how meaning is handled
within the mind. Techniques involve:
- Eye-tracking: Tracing eye movements to make inferences about semantic processing.
- Priming experiments: Studying how seeing one word influences the processing of another.
4. Applications of Semantic Analysis
Semantic analysis has widespread application in modern linguistics and fields beyond
linguistics:
4.1. Natural Language Processing (NLP)
Semantic analysis is a core part of the following functionalities:
- Machine translation: Text translation into another language and preserving sense.
- Sentiment analysis: Detecting emotionality in a text.
- Question answering: Return of correct answers to user queries.
4.2. Lexicography
Semantic analysis directs the practice of dictionary compilation and thesaurus construction
through the determination of word senses and associations.
4.3. Cognitive Science
Semantic analysis assists in understanding how the brain processes language and meaning.
4.4. Artificial Intelligence (AI)
Semantic analysis enables AI systems to understand and generate human-like language,
powering applications like chatbots and virtual assistants.
5. Challenges in Semantic Analysis
Despite its advancement, semantic analysis has certain challenges:
- Ambiguity: Phrases and words have multiple meanings depending on context.
- Cultural and contextual variation: Meaning can vary across cultures and contexts.
- Computational complexity: Computational modeling of meaning remains a difficult task.
6. Future Directions
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 75
The future of semantic analysis lies in the convergence of interdisciplinary approaches,
including:
- Multimodal semantics: Meaning analysis across modalities (e.g., text, images, audio).
- Neurosemantics: Examining the neural basis of meaning.
- Cross-linguistic studies: Cross-linguistic comparison of semantic structures to determine
universal principles.
7. Conclusion
Semantic analysis is a key area of modern linguistics that unites language and meaning. Its
technological applications and theoretical results have changed human communication and
enabled pioneering achievements in technology. With continued research, semantic analysis
will be at the forefront of linguistic and computational innovations.
References:
1. Cruse, D. A. (1986). “Lexical Semantics”. Cambridge University Press.
2. Jackendoff, R. (2002). “Foundations of Language: Brain, Meaning, Grammar,
Evolution”. Oxford University Press.
3. Jurafsky, D., & Martin, J. H. (2020). “Speech and Language Processing”. Pearson.
4. Lakoff, G., & Johnson, M. (1980). “Metaphors We Live By”. University of Chicago
Press.
5. Saeed, J. I. (2016). “Semantics”. Wiley-Blackwell.
