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A “sememe” is an indivisible minimal unit of meaning in linguistics. Manually annotating sememes in words requires a significant amount of time, so automated sememe prediction is often used to improve efficiency. Semantic fields serve as crucial mediators connecting the semantics between words. This paper proposes an unsupervised method for sememe prediction based on the common semantics between words and semantic fields. In comparison to methods based on word vectors, this approach demonstrates a superior ability to align the semantics of words and sememes. We construct various types of semantic fields through ChatGPT and design a semantic field selection strategy to adapt to different scenario requirements. Subsequently, following the order of word–sense–sememe, we decompose the process of calculating the semantic sememe similarity between semantic fields and target words. Finally, we select the word with the highest average semantic sememe similarity as the central word of the semantic field, using its semantic primes as the predicted result. On the BabelSememe dataset constructed based on the sememe knowledge base HowNet, the method of semantic field central word (SFCW) achieved the best results for both unstructured and structured sememe prediction tasks, demonstrating the effectiveness of this approach. Additionally, we conducted qualitative and quantitative analyses on the sememe structure of the central word.

The paper presents results of the ongoing research on creation of the semantic field of the “empire” concept. A semantic field is a collection of content units covering a certain area of human experience and forming a relatively autonomous microsystem with one or several centers. Relations in such microsystems are also called associations. The idea is to extract from data on syntagmatic collocability a set of lexical units connected by systemic paradigmatic relations of various types and strength using distributional analysis techniques. The first goal of the study is to develop methodology to fill a semantic field with lexical units on the basis of morphologically tagged corpora. We were using the Sketch Engine corpus system that implements the method of distributional statistical analysis. Text material is represented by our own corpora in the domain of “empire”. In the course of the work we have acquired lists of items filling the semantic space around the concept of “empire”.

This research deals with revealing various linguistic units: lexemes and collocations within the framework of semantic fields of notional components of the category of deviation that are reflected in the works of professional translators, Michael Glenny, Richard Pevear, and Larissa Volokhonsky of The Master and Margarita by Mikhail Bulgakov in the English language. The author takes the method of conceptual analysis of the linguistic units of expressing different parts of semantic fields of components of the mental unit of deviation in English as the basic one here. Lexemes and collocations are also the focus of a contextual method of reviewing the translators’ approaches to the linguistic units of the work under analysis. Different semantic spheres based on personal and professional experience of Michael Glenny, Richard Pevear, and Larissa Volokhonsky reflect the category of deviation in translations of the novel under study. This category includes the central part and periphery. The central part implies the lexemes and collocations of neutral-bookish style, and the periphery means lexical units of informal style. The author also distinguishes the interpretative field within the semantic fields of the category of deviation. The results given in this article can be a basis for comparative analysis of translations within one language or as a source of a contrastive analysis of literary works.

Research on anglicisms in Polish has nearly a century-long tradition, yet it was Jacek Fisiak’s 1960s–1980s studies on English loanwords that initiated continuous academic interest in anglicisms, coinciding with more intensive English-Polish language contact in post-war Poland. While English loans have been well-researched in the last four decades, the ongoing intensity of English lexical influence on Polish, yielding not only new loans but also new loan types, calls for further studies, especially in the area of quickly developing professional jargons and sociolects. The influx of English-sourced lexis is reflected in the diversity of semantic fields, whose number has grown from 18 (identified in Słownik warszawski 1900–1927) to 45 (Mańczak-Wohlfeld 1995). A semantic field that has been underresearched in studies on Polish anglicisms is the LGBTQ+- related lexis, which has drawn from American English gayspeak, shaped by the post-Stonewall gay rights movement initiated in the 1970s. The language data analysed in this study have been collected in a two-stage procedure, which included manual extraction of anglicisms sourced in a diversified corpus of LGBTQ+-related written texts, published in Polish between 2004 and 2020. The second stage involved oral interviews which served a verification function. The aim of this study is to contribute to the lexicographic attempts at researching English-sourced LGBTQ+-related vocabulary in Polish through its identification, excerption, and classification. Assuming an onomasiological approach to borrowing, we arrange LGBTQ+-related anglicisms on a decreasing foreignness scale to identify the borrowing techniques adopted by the recipient language speakers in the loan nativization process. We also address issues related to the identification and semantics of loans, and sketch areas of research on loan pragmatic functions that need further studies.

Bᾱhῡ’s Abyᾱt opens the doors of spiritual world for the Seekers of God who have been demoted from their spiritual quest. The objective of the study is to interpret Bᾱhῡ’s poetry from the perspective of semantic field theory by pinpointing the semantic content of the synonyms for Murshid such as rāṅjhā, yᾱr, faqīr and ʿᾱshiq and antonyms to Murshid such as ʿᾱlim and bay-adab, a few examples being mentioned from Abyᾱt. Albeit, these words have different denotative signification, yet revealingly, these have the same connotative purport and seem to be related or un-related, as the case may be, to the concept of Murshid. The significance of the study lies in the fact that Bᾱhῡ’s poetry appears to have never been analyzed from semantic field theory viewpoint. Thus, the present research is an endeavor to unearth the underlying principles working behind such untraditional lexical choice as well as the incorporation of the phenomenon of “crazy-wisdom” in Bᾱhῡ’s poetry.

Dignāga's theory of semantics called the \"theory of apoha (exclusion)\" has been criticized by those who state that it may lead to a circular argument wherein \"exclusion of others\" (anyāpoha) is understood as mere double negation. Dignāga, however, does not intend mere double negation by anyāpoha. In his view, the word \"cow\" for instance, excludes those that do not have the set of features such as a dewlap, horns, and so on, by applying the semantic method called componential analysis. The present paper aims to prove this by referring to the fragments quoted by Jinendrabuddhi and Siddhasenagani. Dignäga logically proves that the denotation of the referent Q by the word \"P\" cannot be derived from the joint presence (anvaya) of \"P\" with Q. Instead, he derives it from the joint absence (vyatireka) of \"P\" with the nonexistence of Q. Anyāpoha is nothing but verbal vyatireka. Componential analysis is used for describing what is to be excluded. Dignāga draws taxonomic hierarchies of words based on their customary use, and assumes componential analysis to operate in the background of the hierarchies formed in semantic fields, stating that a general term is restricted to having the same reference with one of its specific terms insofar as the former is connected (yukta) with the characteristics of the latter's referent. Moreover, he states that a proper name also denotes its referent by excluding those that do not have the cluster of a certain number of qualities.

What is a metaphoric picture of an evil person made of? In a study devoted to the development of the ability to use metaphorical descriptions of humans, the semantic fields of four target metaphors - Human-Swamp, Human-Snake, Human-Knife, and Human-Nettle - were established and compared. Subjects (365 young adults) were asked to decipher the metaphors’ meanings. The results were obtained mainly by qualitative analysis, with frequency analysis of clusters containing synonymous meanings. The results indicate that when creating imaginary characteristics of evil people, young adults seem to be more concerned about the possibility of suffering verbal harassment (most commonly: vulgarity, mockery, gossip, jeering) than the threat of actual physical assault. The results may prove useful for developmental comparisons.

Background Primary progressive aphasias (PPA) and Alzheimer's disease (AD) are a group of neurodegenerative diseases defined by a gradual decline of linguistic and neuro‐cognitive abilities. PPA are traditionally classified into four predominant variants: nonfluent‐agrammatic (PPA‐NAv, semantic (PPA‐Sv), logopenic (PPA‐Lv) and mixed (PPA‐Mv). We aimed at phenotyping PPA and AD in native speakers of Moroccan Arabic. Method The study included 20 Arabic‐speaking participants: 14 with Alzheimer's Disease (4 mild, 10 moderate‐severe) and 6 with PPA (2 PPA‐NAv; 1 with PPA‐Sv; 2 with PPA‐Lv and 1 with PPA‐Mv). Participants underwent neuropsychological assessments, including the Moroccan version of the Mini‐Linguistic State Examination (MLSE), to evaluate various aspects of language function. The study employed the Kussmaul's model (Behforuzi et al., 2013) for neurocognitive analysis. Result According to the Kussmaul's connexionist model, a significant number of patients in this case series with AD may retain visual recognition units, which facilitate access to a relatively preserved semantic‐conceptual field (8‐F‐3). Consequently, the phenomenon of dysnomia is likely attributed to challenges in accessing the phonological lexicon (8‐G‐4). In contrast, individuals diagnosed with nfv‐PPA also exhibit dysnomia, which is likely the result of a disconnection among the semantic‐conceptual module, the fronto‐subcortical intentional system, the phonetic processor, and the output lexicon (3‐H‐4 or 7‐K‐3‐H‐4). Conversely, the patients with Lv‐PPA exhibited progression to PPA+ characterized by episodic and semantic memory impairments. Conclusion This study offers a viewpoint on the neurolinguistic features of AD and PPA based on Kussmaul's model. It highlights the deterioration of syntactic abilities and semantics within MLSE, which contrasts with phonology and motor speech.

Cross-linguistic research on lexical-semantic groups (LSGs) often remains predominantly qualitative and fragmented, which makes it difficult to compare universal and culture-specific patterns across languages in a systematic way. This study examines four LSGs emotions, colours, verbs of motion, and kinship terms in Ukrainian, English, and German in order to clarify how their core and periphery are structured in contemporary language use. Conceptually, the study is situated within cognitive semantics and prototype theory, treating core–periphery structure as a frequency-based approximation of shared prototypes and peripheral extensions. The empirical basis consists of a questionnaire survey of 300 respondents (100 per language group), combining frequency ratings with choices among near-synonymous items. Quantitative analysis (core ≥ 75% of respondents) is complemented by qualitative interpretation of polysemy and cultural associations. The results show that in the emotional domain Ukrainians most frequently actualize радість ‘joy’, whereas English and German speakers foreground negative emotions such as anger/Ärger and fear/Angst. In the colour group, a shared core is formed by red, blue, and green, with minor differences in the salience of yellow. For verbs of motion, йти/go/gehen constitutes a universal core, while English and German display higher frequencies of transport-related verbs (ride/fahren). Kinship terms (mother, father, brother, sister) form the most stable core across all three languages. Overall, the study demonstrates how LSGs simultaneously reflect universal cognitive categories and culturally conditioned profiles of salience and contributes to cognitive and contrastive semantics by offering an empirically grounded, frequency-based core–periphery model with applications for contrastive semantics, translation, and intercultural language pedagogy.

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.