Semantic Categories Research Articles

IeMTLF: Interaction-enhanced Multi-Task Learning Framework for next location prediction

Location-Based Services (LBSs) provide spatial and semantic information of various locations, thanks to the advances in mobile devices and commercial map services. However, most existing studies on next location prediction neglect the semantic information and do not investigate the effects of different levels of semantic granularity. To address these issues, we propose an Interaction-enhanced Multi-Task Learning Framework (IeMTLF) that jointly predicts the next location and its semantic category and leverages higher-order feature interactions to enrich the semantic learning process. Moreover, we examine the effect of two different levels of semantic granularity, namely, specific and abstract, on prediction performance. We conducted extensive experiments on two real-world datasets and demonstrate that IeMTLF outperforms seven baselines on all evaluation metrics, with a maximum gain of more than 13% for Acc@5 compared to the state-of-the-art method. Our implementation code is available at https://github.com/mrc-wyh/IeMTLF.

The semantic content of concrete, abstract, specific, and generic concepts

Abstract Abstraction processes involve two variables that are often confused with one another: concreteness (banana versus belief) and specificity (chair versus furniture or Buddhism versus religion). Researchers are investigating the relationship between them, but many questions remain open, such as: What type of semantics characterizes words with varying degrees of concreteness and specificity? We tackle this topic through an in-depth semantic analysis of 1049 Italian words for which human-generated concreteness and specificity ratings are available. Our findings show that (as expected) the semantics of concrete and abstract concepts differs, but most interestingly when specificity is considered, the variance in concreteness ratings explained by semantic types increases substantially, suggesting the need to carefully control word specificity in future research. For instance, mathematical concepts (phase) are on average abstract and generic, while behavioral qualities (arrogant) are on average abstract but specific. Moreover, through cluster analyses based on concreteness and specificity ratings, we observe the bottom-up emergence of four subgroups of semantically coherent words. Overall, this study provides empirical evidence and theoretical insight into the interplay of concreteness and specificity in shaping semantic categorization.

Expressive vocabulary word categories of children who are deaf and hard-of-hearing.

This study investigated the acquisition of early expressive vocabulary among young children who are deaf and hard-of-hearing (DHH; n = 68) using auditory technology (hearing aids and cochlear implants). Parents completed a standardized vocabulary checklist, which allowed analyses of (i) the size of their child's spoken vocabulary; (ii) composition of the expressive lexicon (e.g., parts of speech such as nouns and verbs; semantic categories such as routines and body parts); and (iii) demographic and audiologic factors (e.g., chronologic age, degree of hearing access) potentially associated with these metrics. Young children who are DHH and use auditory technology acquired fewer spoken words than peers with typical hearing (TH) matched for chronologic age but more spoken words than peers with TH matched for listening experience. Action verbs-not nouns-significantly increased the odds of a child who is DHH achieving a vocabulary quotient within the normative range. These findings support the exploration of early expressive vocabulary size and composition-especially the number of active verbs-to guide clinical management and decision-making for young children who are DHH.

The role of vision in the acquisition of words: Vocabulary development in blind toddlers.

What is vision's role in driving early word production? To answer this, we assessed parent-report vocabulary questionnaires administered to congenitally blind children (N=40, Mean age=24months [R: 7-57months]) and compared the size and contents of their productive vocabulary to those of a large normative sample of sighted children (N=6574). We found that on average, blind children showed a roughly half-year vocabulary delay relative to sighted children, amid considerable variability. However, the content of blind and sighted children's vocabulary was statistically indistinguishable in word length, part of speech, semantic category, concreteness, interactiveness, and perceptual modality. At a finer-grained level, we also found that words' perceptual properties intersect with children's perceptual abilities. Our findings suggest that while an absence of visual input may initially make vocabulary development more difficult, the content of the early productive vocabulary is largely resilient to differences in perceptual access. RESEARCH HIGHLIGHTS: Infants and toddlers born blind (with no other diagnoses) show a 7.5 month productive vocabulary delay on average, with wide variability. Across the studied age range (7-57months), vocabulary delays widened with age. Blind and sighted children's early vocabularies contain similar distributions of word lengths, parts of speech, semantic categories, and perceptual modalities. Blind children (but not sighted children) were more likely to say visual words which could also be experienced through other senses.

New names of dishes, beverages, and foods in the Polish language as a reflection of the contemporary nutritional trends (a lexicographic research)

This article presents and overview of the contemporary nutritional trends established in the 21st-century units of the nominative grid of the lexical and semantic field POTRAWY, NAPOJE I PRODUKTY SPOŻYWCZE (DISHES, BEVERAGES, AND FOODS) of the Polish language. It bears characteristics of a research, since it is based exclusively on an analysis of the lexis that is being recorded in two dictionaries that are being developed on an ongoing basis and that is absent from the 2005 monographic study of culinary lexis. However, some most general regularities are noticeable. In the first place, the strong glocalisation and health-promoting naming trends present in this field at least since the last decade of the 20th century have prevailed. They are complemented by more recent processes, resulting in the emergence of semantic categories that are small in scope and comprise a few components in the vocabulary. The most prominent among these are ecological views (vegetarianism and veganism) and the treatment of food as a source of pleasure (sensual terms, stimulant names, comfort food).

Impulsive and compulsive reading comprehension in the prison population

BackgroundDevelopmental dyslexia is characterized by reading and writing deficits that persist into adulthood. Dyslexia is strongly associated with academic underachievement, as well as impulsive, compulsive, and criminal behaviors.The aims of this study were to investigate impulsive or compulsive reading comprehension, analyzing the differences in reading errors between two distinct groups —one with Antisocial Personality Disorder (ASPD) and another with Obsessive–Compulsive Personality Disorder (OCPD) and examine their correlation with criminal behavior within a prison population.MethodsWe gathered data from 194 participants: 81 with ASPD and 113 with OCPD from a prison center. Participants took part in interviews to gather data on demographic, criminal, and behavioral data. Additionally, the participants underwent various assessments, including the International Examination for Personality Disorders; Symptom Inventory, and Battery for the Assessment of Reading Processes in Secondary and High School – Revised.ResultsOur analysis revealed differences in reading skills between the ASPD and OCPD groups. Specifically, the OCPD group showed poorer performance on lexical selection, semantic categorization, grammar structures, grammatical judgements, and expository comprehension when compared with the ASPD group. Conversely, the OCPD group obtained higher scores on narrative comprehension relative to the ASPD group.ConclusionsThe OCPD group showed slow lexical-phonological coding and phonological activation.

A CORPUS-BASED STUDY OF REPORTING VERBS IN SHORT ESSAY

The present research aims to analyze the use of reporting verbs in short essays written by undergraduate students of English major at a university in Indonesia. The study focuses on the frequency analysis of reporting verbs across four semantic categories, namely Argue verbs (e.g., argue, suggest, write, etc.), Find verbs (e.g., find, observe, discover, etc.), Show verbs (show, demonstrate, reveal, etc.) and Think verbs (e.g., think, assume, know, etc.). To achieve this, a mixed-method research design and a corpus-based approach are employed. The results show that the students predominantly use the Think verbs category, followed by Argue, Show, and Find verbs. It suggests that students may focus more on expressing their beliefs and opinions rather than providing evidence or confirming their claims. Moreover, the study highlights the implications of the finding, indicating that the overuse of verbs from the Think verbs category may indicate a lack of evidence-based reasoning in the students’ short essays.

Rethinking ethics in AI policy: a method for synthesising Graham’s critical discourse analysis approaches and the philosophical study of valuation

ABSTRACT Here I use aspects of Phil Graham’s discourse analytical work to examine forms of e/valuations and critically analyse the formulation of truths in the constitution of Artificial Intelligence (hereafter, AI). This paper focuses on two 2019 documents: Ethics guidelines for trustworthy AI (AI HLEG, 2019a) and Policy and investment recommendations for trustworthy AI (AI HLEG, 2019b). My aim here is to provide a timely contribution to contemporary philosophical–methodological innovations in documenting the constellation of values that are prefigured in human-centric constructions of AI. The analysis is informed primarily by principles from Graham’s distinctive approach to critical discourse analysis (CDA), which I take to be the philosophical study of valuation. In that approach, political economy is taken as a composite formulation of values whereby neo-liberalism is discursively entwined and progressed through a system of principles of e/valuation. This paper presents a roadmap to demonstrate the usefulness of a philosophically grounded interdisciplinary piece of linguistic research through which evaluative semantic categories can be usefully synthesised with CDA to systematically expose the assumptions which underpin current truth claims and values – in this case, about and around the ethics of AI.

Video process detection for space electrostatic suspension material experiment in China’s Space Station

The purpose of video process detection is to identify key processes of interest and associated semantic categories, which is an important computer vision task for localizing key processes in space science experiments. To detect experimental processes in space electrostatic suspension material experiment on China’s Space Station, we propose a Slow-and-Fast Dual-Stream Network. It extracts multi-scale temporal semantic information by designing a Slow-and-Fast Temporal Stream structure and effectively detects the experimental processes with various temporal scales. In addition, the Two Path Temporal Pyramid Network (TP2N) enhances the bi-direction flow among multi-scale features on the temporal dimension to alleviate boundary sensitivity. The validation experiments on our established dataset demonstrate the effectiveness of our proposed method with 62.43% mAP, outperforming the related state-of-the-art temporal action localization methods with nearly 7% mAP gain. This paper is the first application of deep learning in space electrostatic suspension material experiments on China’s Space Station and further provides important technical support for the identification of key processes in space science experiments.

The Cerebellar Cognitive Affective/Schmahmann Syndrome Scale in Spinocerebellar Ataxias.

The Cerebellar Cognitive Affective/Schmahmann Syndrome (CCAS) manifests as impaired executive control, linguistic processing, visual spatial function, and affect regulation. The CCAS has been described in the spinocerebellar ataxias (SCAs), but its prevalence is unknown. We analyzed results of the CCAS/Schmahmann Scale (CCAS-S), developed to detect and quantify CCAS, in two natural history studies of 309 individuals Symptomatic for SCA1, SCA2, SCA3, SCA6, SCA7, or SCA8, 26 individuals Pre-symptomatic for SCA1 or SCA3, and 37 Controls. We compared total raw scores, domain scores, and total fail scores between Symptomatic, Pre-symptomatic, and Control cohorts, and between SCA types. We calculated scale sensitivity and selectivity based on CCAS category designation among Symptomatic individuals and Controls, and correlated CCAS-S performance against age and education, and in Symptomatic patients, against genetic repeat length, onset age, disease duration, motor ataxia, depression, and fatigue. Definite CCAS was identified in 46% of the Symptomatic group. False positive rate among Controls was 5.4%. Symptomatic individuals had poorer global CCAS-S performance than Controls, accounting for age and education. The domains of semantic fluency, phonemic fluency, and category switching that tap executive function and linguistic processing consistently separated Symptomatic individuals from Controls. CCAS-S scores correlated most closely with motor ataxia. Controls were similar to Pre-symptomatic individuals whose nearness to symptom onset was unknown. The use of the CCAS-S identifies a high CCAS prevalence in a large cohort of SCA patients, underscoring the utility of the scale and the notion that the CCAS is the third cornerstone of clinical ataxiology.

Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis.

In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes. When this translation process is inaccurate or incomplete, it can lead to failed segmentation results where the model struggles to correctly classify pixels into the appropriate semantic categories. The study proposed a conditional Generative Adversarial Network (cGAN), for creating high-resolution urban maps from satellite images. The method combines semantic and spatial data using cGAN framework to produce realistic urban scenes while maintaining crucial details. To assess the performance of the proposed method, extensive experiments are performed on benchmark datasets, the ISPRS Potsdam and Vaihingen datasets. Intersection over Union (IoU) and Pixel Accuracy are two quantitative metrics used to evaluate the segmentation accuracy of the produced maps. The proposed method outperforms traditional methods with an IoU of 87% and a Pixel Accuracy of 93%. The experimental findings show that the suggested cGAN-based method performs better than traditional techniques, attaining better segmentation accuracy and generating better urban maps with finely detailed information. The suggested approach provides a framework for resolving the image-to-image translation difficulties in urban scene segmentation, demonstrating the potential of cGANs for producing excellent urban maps from satellite data.

Maternal perception of factors that interfere with breastfeeding of preterm newborns.

to verify the association between the perception of mothers of premature infants regarding the features that may interfere with breastfeeding and the mother's socioeconomic data, pregnancy and the baby's clinical data. observational, descriptive and analytical quali-quantitative cross-sectional study. One hundred and fourteen mothers of premature infants were included and data were collected through questionnaires, applied at hospital discharge, and analysis of medical records. Maternal responses about the interference observed in the breastfeeding process were categorized by content analysis and associated with socioeconomic, pregnancy and baby data. the mothers' perceptions regarding the factors that interfere with the baby's feeding at the mother's breast were divided into four semantic categories: clinical and/or physical conditions of the baby; clinical, physical and/or psycho-emotional conditions of the mother; support network; and strategies for initiating and/or maintaining breastfeeding. Education, paternal presence, having other children and having breastfed them were associated with the maternal perception that their clinical, physical and/or psycho-emotional conditions interfere with breastfeeding. In addition, the support network was associated with exclusive breastfeeding at discharge. education, paternal presence, multiparity and having breastfed previous children influenced the maternal perception that their clinical, physical and/or psycho-emotional conditions interfere with breastfeeding. In addition, mention of the support network was associated with exclusive breastfeeding at discharge.

DATran: Dual Attention Transformer for Multi-Label Image Classification

Multi-label image classification is a fundamental yet challenging task, which aims to predict the labels associated with a given image. Most of previous methods directly exploit the high-level features from the last layer of convolutional neural network for classification. However, these methods cannot obtain global features due to the limited size of convolutional kernels, and they fail to extract multi-scale features to effectively recognize small-scale objects in the images. Recent studies exploit the graph convolution network to model the label correlations for boosting the classification performance. Despite substantial progress, these methods rely on manually pre-defined graph structures. Besides, they ignore the associations between semantic labels and image regions, and do not fully explore the spatial context of images. To address above issues, we propose a novel Dual Attention Transformer (DATran) model, which adopts a dual-stream architecture that simultaneously learns spatial and channel correlations from multi-label images. Firstly, in order to solve the problem that current methods are difficult to recognize small-size objects, we develop a new multi-scale feature fusion (MSFF) module to generate multi-scale feature representation by jointly integrating both high-level semantics and low-level details. Secondly, we design a prior-enhanced spatial attention (PSA) module to learn the long-range correlation between objects from different spatial positions in images to enhance the model performance. Thirdly, we devise a prior-enhanced channel attention (PCA) module to capture the inter-dependencies between different channel maps, thus effectively improving the correlation between semantic categories. It is worth noting that PSA module and PCA module complement and promote each other to further augment the feature representations. Finally, the outputs of these two attention modules are fused to obtain the final features for classification. Performance evaluation experiments are conducted on MS-COCO 2014, PASCAL VOC 2007 and VG-500 datasets, demonstrating that DATran model achieves better performance than current state-of-the-art models.

Types of use of peripheral forms of the creative case in the aspect of adverbialization (the substantive word form "in a whisper")

The stepwise nature of adverbial transposition of nouns without prepositions is investigated. The object of study is the forms of the creative case of nouns without prepositions, representing the first stage of transposition into adverbs, the subject of consideration is combinatorics and the proportion of substantive and adverbial features in the structure of word forms of the "whisper" type, functioning in the zone of the periphery of the noun class. The relevance of the research is due to the need to develop such a field of scientific research as the transpositional grammar of the Russian language, as well as to identify patterns of functioning of linguistic units in transitional zones between parts of speech and interparticle semantic and syntactic categories. The purpose of the article is a comprehensive analysis of substantive word forms that demonstrate different degrees of manifestation of signs interacting with the functional adverbialization of nouns and adverbs on two stages of the noun zone. Within the framework of the proposed approach to syncretic formations, methods of oppositional analysis (with an adverbial scale), elements of component, distributive analysis, and linguistic experiment are used. The novelty of the study consists in the description of a formalized methodology for establishing the stages of adverbialization of substantive word forms, demonstrating a different degree of departure from nouns and approximation to adverbs. It is established that the substantive word form "whisper", for example, is characterized by a multi-stage transposition into an adverbial class. In the zone of the periphery of nouns, she makes the first, actually syntactic step towards adverbs. It is associated with its use first in the syncretic function of complement and circumstance, and then only in the function of circumstance. The presence of dependent words in the form of adjectival and subsubstantial distributors, as well as the function of object complement, restrain the further advancement of the word form "in a whisper" towards the class of adverbs. The loss of object meaning in the word form under consideration is associated with a shift at the level of verbal subordination: weak control as a way of connecting the word form "in a whisper" with the main word is replaced by case adjunction. Object-circumstantial relations are transformed into circumstantial ones. But the main categorical features of nouns in the zone of the periphery of this class of words are preserved. The theoretical significance of the work lies in the fact that it contains an understanding of transitional phenomena in the field of parts of speech in a systemic linguistic aspect. The practical significance of the research is determined by the possibility of applying its results in teaching linguistic disciplines – Russian, general linguistics, theoretical grammar.

LINGUO-AXIOLOGICAL CHARACTERISTICS OF POLITICO-DIPLOMATIC SPEECHES

The article is devoted to studying the political speeches of Ukrainian presidents (the last decade) at the United Nations (UN), particularly the theoretical and methodological foundations of studying the specifics of political discourse in international communication, namely, in the politico-diplomatic aspect. This study aims to deepen the theoretical understanding of political discourse and reveal the linguistic aspects that determine the communicative nature of political speeches at the UN. In particular, the authors pay attention to the linguistic aspect of politicians' speeches in English at the UN, and conduct a detailed analysis of the axiological language tools used by the Ukrainian presidents to express their assessments and views. The use of specific lexical units that help structure and formulate political ideas, considering the international nature of diplomatic relations, is considered. Understanding the linguistic axiological features of political and diplomatic speech helps to improve communication between representatives of different countries. The study of effective ways of linguistic influence through lexical units with an evocative load can also help to reduce misunderstandings in international relations. This research has used a wide range of scientific methods, including content analysis of the English-language speeches of politicians on the background of political and diplomatic discourse using structural and semantic identification and categorization of lexical features, functional and pragmatic analysis of linguistic means of axiological nature, as well as the method of generalization and systematization to summarize and arrange the information obtained in the course of the study.

Trends in the development of vocabulary for emotion and cognition in English: A millennial perspective

Existing research has shown that there is much variation in diachronic development and borrowability rates across different semantic fields in English, but most work up to now has focused on specialized or technical vocabulary, rather than fields associated with how people express and conceptualize their feelings and thoughts. While some studies have explored the historical evolution of the emotion lexicon (e.g., Diller [2014]) and, to a lesser extent, the cognition lexicon (e.g., Kiricsi [2010]), the contrastive study of these semantic categories has yet to be a focus in the research landscape. Therefore, this paper aims to explore the development of the cognition and emotion lexicon over a period of thousand years, from Old to Present-Day English. We compare the emergence of words in these two semantic fields across three key dimensions: semantic subfields, source languages of roots and morphological structure. For this purpose, we created a sample of approx. 1400 nouns on the basis of the Historical Thesaurus of English (HTE; categories: 02.01 mental capacity and 02.04 emotion) and analysed their etymology with the help of the Oxford English Dictionary. To annotate the lexical items in our sample, we used the methods of formal-morphological analysis (e.g., Tyschenko [2003]; Krykoniuk [2022]) and semantic analysis (e.g., Pons-Sanz [2022]; HTE [2023]). We analyse our sample with clustering techniques (i.e., Principal Component Analysis and Correspondence Analysis) to determine general trends across the two lexicons (e.g. a greater involvement of Latin roots and compounding in the formation of the cognition lexicon, and Norse-derived roots and conversion in coinage of nouns referring to emotion), as well as specific features of each period of the English language (e.g. the significant impact that the Middle English period had on the lexicalization of memory, fear, compassion, pleasure and suffering, and the early integration of French-derived terms, as suggested by word-formation processes).

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the coding of complex semantic categories in large datasets via supervised machine learning. However, three characteristics so far impeded the widespread adoption of the methods in the applying disciplines: the dominance of English language models in NLP research, the necessary computing resources, and the effort required to produce training data to fine-tune PLMs. In this study, we address these challenges by using a multilingual transformer model in combination with the adapter extension to transformers, and few-shot learning methods. We test our approach on a realistic use case from communication science to automatically detect claims and arguments together with their stance in the German news debate on arms deliveries to Ukraine. In three experiments, we evaluate (1) data preprocessing strategies and model variants for this task, (2) the performance of different few-shot learning methods, and (3) how well the best setup performs on varying training set sizes in terms of validity, reliability, replicability and reproducibility of the results. We find that our proposed combination of transformer adapters with pattern exploiting training provides a parameter-efficient and easily shareable alternative to fully fine-tuning PLMs. It performs on par in terms of validity, while overall, provides better properties for application in communication studies. The results also show that pre-fine-tuning for a task on a near-domain dataset leads to substantial improvement, in particular in the few-shot setting. Further, the results indicate that it is useful to bias the dataset away from the viewpoints of specific prominent individuals.

Machine Learning-Enhanced ORB Matching Using EfficientPS for Error Reduction

Abstract The study comes up with a new way to improve the accuracy of image matching in binocular vision systems, especially those that are mounted on vehicles. It combines machine learning with the ORB (Oriented FAST and Rotated BRIEF) image-matching algorithms. Standard ORB matching frequently encounters mismatches in complex and repetitive environments. To minimize false positives in matches, our strategy utilizes the EfficientPS (Efficient Panoptic Segmentations) algorithm, a panoramic segmentation technique that uses machine learning in conjunction with ORB. The procedure begins with the EfficientPS approach, which delivers fine-grained and efficient segmentation of images, assigning semantic category labels and unique identifiers to each pixel. The ORB feature point matching process is refined using semantic data to filter out mismatches between foreground objects and the background effectively. This machine-learning-augmented method significantly decreases the frequency of erroneous matches in intricate settings. Empirical findings from the KITTI dataset demonstrate that in non-targeted environments, the accuracy of our proposed method (0.978) is marginally less than that of LoFTR (0.983). Still, it surpasses other methods when utilizing 50 ORB parameters. In more intricate situations, such as multi-target scenarios with an increased number of ORB parameters (200), our method maintains a high level of accuracy (0.883), outperforming the conventional ORB (0.732) and rivaling the performance of DL-BDLMR (0.790) and ORB-MFD-FPMC (0.835). Our method’s processing time is competitive and slightly higher than the standard ORB, but it improves accuracy. In scenarios without targets and with single targets, our method’s processing time (0.195 seconds and 0.211 seconds, respectively) is greater than that of ORB and ORB-MFD-FPMC. Yet, it is significantly lower than that of LoFTR. In multi-target scenarios, our method’s processing time (0.226 seconds) is considerably better than LoFTR’s (0.445 seconds), effectively balancing processing speed and accuracy. We highlight the efficacy of incorporating machine learning to enhance the robustness of image-matching algorithms in dynamic and complex environments.

Does semantic similarity affect immediate memory for order? Usually not, but sometimes it does.

Recall performance in working memory (WM) is strongly affected by the similarity between items. When asked to encode and recall list of items in their serial order, people confuse more often the position of similar compared to dissimilar items. Models of WM explain this deleterious effect of similarity through a problem of discriminability between WM representations. In contrast, when lists of items that are all semantically similar are compared to lists of dissimilar items, semantic similarity does not negatively impact order memory, questioning the idea that semantic information is part of the WM content. This study reports four experiments in which semantic similarity was manipulated using lists composed of multiple semantic categories. These experiments revealed two main patterns. First, semantic similarity can increase, rather than decrease, order memory. Second, semantic knowledge reliably constrains the way items migrate; when migrating, items tend to do so more often toward the position of other similar items, rather than migrating toward other dissimilar items. These results challenge the way current models of WM represent similarity. The plausibility of different theoretical accounts and mechanisms is discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Existential constructions in the Yakut language: a model of causation of the beginning of existence

The article is devoted to the study of models of existential constructions in the Yakut language. It examines the causation model of the beginning of existence in accordance with the principles of functional and semantic analysis. The relevance of this article is due to modern approaches in the study of the Yakut language as a system represented by multi-level means of its expression. The sentence as the main syntactic unit is displayed in a model that "combines the invariants of the minimal structural schemes that ensure the completeness of the sentence, and the generalized propositions associated with them." The structural scheme of the causation model of the beginning of existence in the Yakut language consists of a subject, an object and a predicate of an existential type expressed by verbs of the initial phase of being, existence. The typical meaning of the model is "to give the beginning to any event, phenomenon, contribute to the appearance, emergence of someone or something." The basis of this study is a functional-semantic approach to the study of language phenomena, which assumes a holistic organization of universal semantic categories. The study uses the continuous sampling method in the formation of material from lexicographic sources, the modeling method, and the glossing method to represent illustrative material. The presentation of the causation model of the beginning of existence as a component of the functional and semantic field of being in the Yakut language is new. It is revealed that the subject and object of the considered model can be stated by a noun, a pronoun, denoting an animate or inanimate person. In accordance with the forms of the subject and object included in the structural composition of the specified model, variants of its structural type are determined. It is established that in this model, such verbs of being as tөrүtte ‛to found, create something; to start something, olohtoo ‛ to inhabit, settle, infuse; can be used as a predicate, to establish; justify’, saҕalaa‛to start any action; to begin’, tөrөt‛to give birth.