Semantic Associations Research Articles

Music, Memory, and Imagination

This article argues that the capacity of music to reliably cue both autobiographical memories and fictional imaginings can be leveraged to better understand the relationship and interdependence between memory and imagination more generally. The multiple levels involved in musical engagement provide a rich forum for investigating how emotional, semantic, and contextual associations with musical cues influence both memories and imaginings. Moreover, musical excerpts are extended in time and can influence the trajectory of a memory or imagining dynamically as it develops, allowing for a more precise manipulation of the implied semantic space. Because music’s uses and contextual associations are culturally constrained, and culture can be shared, autobiographical memories and fictional imaginings cued by music can show surprising similarities among individuals from the same culture. This article surveys the research on music-evoked autobiographical memories and music-evoked fictional imaginings, proposing a framework for bringing these separate strands of work together to shed light on larger questions about shared underlying mechanisms.

Thangka Image—Text Matching Based on Adaptive Pooling Layer and Improved Transformer

Image–text matching is a research hotspot in the multimodal task of integrating image and text processing. In order to solve the difficult problem of associating image and text data in the multimodal knowledge graph of Thangka, we propose an image and text matching method based on the Visual Semantic Embedding (VSE) model. The method introduces an adaptive pooling layer to improve the feature extraction capability of semantic associations between Thangka images and texts. We also improved the traditional Transformer architecture by combining bidirectional residual concatenation and mask attention mechanisms to improve the stability of the matching process and the ability to extract semantic information. In addition, we designed a multi-granularity tag alignment module that maps global and local features of images and text into a common coding space, leveraging inter- and intra-modal semantic associations to improve image and text accuracy. Comparative experiments on the Thangka dataset show that our method achieves significant improvements compared to the VSE baseline method. Specifically, our method improves the recall by 9.4% and 10.5% for image-matching text and text-matching images, respectively. Furthermore, without any large-scale corpus pre-training, our method outperforms all models without pre-training and outperforms two out of four pre-trained models on the Flickr30k public dataset. Also, the execution efficiency of our model is an order of magnitude higher than that of the pre-trained models, which highlights the superior performance and efficiency of our model in the image–text matching task.

Multi-granularity retrieval of mineral resource geological reports based on multi-feature association

Massive geologic report contains all kinds of multimodal geologic data information (geologic text, geologic maps, geologic tables, etc.), which contain a lot of rich geologic basic knowledge and expert experience knowledge about rocks and minerals, stratigraphic structure, geologic age, geographic location, and so on. Accurate retrieval of specific information from massive geologic data has become an important need for geologic information retrieval. However, the majority of existing research primarily revolves around extracting and associating information at a single granularity to facilitate geological semantic retrieval, which ignores many potential semantic associations, leading to ambiguity and fuzziness in semantic retrieval. To solve these problems, this paper proposes a multi-granularity (document-chapter-paragraph) geological information retrieval framework for accurate semantic retrieval. The framework firstly extracts topic feature information, spatiotemporal feature information, figure and table feature information based on the multi-granularity of geological reports. Then, an improved apriori algorithm is used to mine and visualize the associations among the feature information to discover the semantic associations of the geological reports at multiple levels of granularity. Finally, experiments are designed to validate the application of the proposed multi-granularity information retrieval framework on the accurate retrieval of geological reports. The experimental results show that the proposed multi-granularity information retrieval framework in this paper can dig deeper into underlying geo-semantic information and realize accurate retrieval.

Three-stage Transferable and Generative Crowdsourced Comment Integration Framework Based on Zero- and Few-shot Learning with Domain Distribution Alignment

Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes from the crowdsourced comments can provide valuable feedback for marketing strategy adjustment or product-quality monitoring. Unfortunately, the scarcity of annotated ground truth on the integrated comment, or the limited gold integration reference, has incurred the infeasibility of the regular supervised-learning-based comment integration. To resolve this problem, in this article, inspired by the principle of Transfer Learning, we propose a three-stage transferable and generative crowdsourced comment integration framework ( TTGCIF ) based on zero-and-few-shot learning with the support of domain distribution alignment. The proposed framework aims at generating abstractive integrated comment in target domain via the enhanced neural text generation model, by referring the available integration resource in related source domains, to avoid the exhausted effort on resource annotation devoted to the target domain. Specifically, at the first stage, to enhance the domain transferability, representations on the crowdsourced comments have been aligned up between the source and target domain, by minimizing the domain distribution discrepancy in the kernel space. At the second stage, Zero-shot comment integration mechanism has been adopted to deal with the dilemma that none of the gold integration reference may be available in target domain. In other words, taking the sample-level semantic prototype as input, the enhanced neural text generation model in TTGCIF is trained to learn data semantic association among different domains via semantic prototype transduction, so that the “ unlabeled ” crowdsourced comments in target domain can be associated with existing integration references in related source domains. At the third stage, based on the parameters trained at the second stage, fast domain adaptation mechanism in a Few-shot manner has also been adopted by seeking most potential parameters along the gradient direction constrained by instances across multiple source domains. In this way, parameters in TTGCIF can be sensitive to any alteration on training data, ensuring that even if only few annotated resource in target domain are available for “Fine-tune,” TTGCIF can still react promptly to achieve effective target domain adaptation. According to the experimental results, TTGCIF can achieve the best transferable product comment integration performance in target domain, with fast and stable domain adaption effect depending on no more than 10% annotated resource in target domain. More importantly, even if TTGCIF has not been fine-tuned on the target domain, yet by referring to the available integration resource in related source domains, the integrated comments generated by TTGCIF on the target domain are still superior to those generated by models already fine-tuned on the target domain.

Horizoning recent trends in the security of smart cities: Exploratory analysis using latent semantic analysis

The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments.

Integrative Development of Modern and Contemporary Literary Works and Traditional Culture Combined with Semantic Association Network Modeling

Abstract In recent years, the development of network novels has pushed traditional cultures such as Taoism, Buddhism, and Confucianism to a peak of attention, making traditional cultures constantly emphasized and excavated, forming an important social subject. The evaluation of textual entities with respect to the integration of modern and contemporary literary works and traditional culture is supported by a semantic association network model proposed in this paper. The model fully exploits the heterogeneity of semantic associations between modern and contemporary literary works and traditional culture and utilizes the RSS model to extract the emotional words of traditional cultural elements in the text of literary works in order to decide the global importance of traditional cultural elements in literary works. Finally, the effectiveness of the method was verified in the dataset. The results of the study show that in the dataset of modern and contemporary literary works, the traditional cultures with the highest semantic association strengths are Taoist culture (0.657), Confucian culture (0.583), and folk culture (0.651), respectively. The incorporation of traditional culture by writers in their literary works can result in a mutual achievement and development of literary works and traditional culture.

Shorter sleep duration and lesser sleep efficiency are associated with poorer memory functions among non-demented, middle-aged, and older rural Indians.

Sleep is known to be involved in cognitive processes, such as memory encoding and consolidation, and poor sleep is a potential risk factor for dementia. This study aims to investigate the effect of sleep quality on memory functions among middle-aged and older adults from a rural Indian population. Participants were non-demented, rural Indians (≥45 years) from an ongoing, prospective, aging cohort study, namely Srinivaspura Aging, NeuroSenescence, and COGnition (SANSCOG) study. Cross-sectional (baseline) data on seven sleep dimensions was obtained using the Pittsburgh Sleep Quality Index (PSQI). Memory functions were assessed using immediate recall, delayed recall, name-face association, and semantic association from a culturally validated, computerized, neurocognitive test battery. Linear regression models, unadjusted and adjusted for cognitive status, age, sex, and depression were used to analyze the association between each sleep dimension and the memory tests. A total of 1195 participants, with a mean age of 57.10 years, were included. Out of the seven sleep dimensions of the PSQI, only two dimensions, namely sleep duration and sleep efficiency, were significantly associated with memory functions. In the fully adjusted model, shorter sleep duration was significantly associated with poorer performance in delayed recall, and lesser sleep efficiency was significantly associated with poorer delayed recall and semantic association performance. Specific sleep characteristics appear to influence memory functions in aging Indians well before the onset of dementia. In the backdrop of the non-availability of a definitive treatment for dementia, promptly identifying and addressing these problems could be an effective, community-level strategy for preventing dementia.

CSFwinformer: Cross-Space-Frequency Window Transformer for Mirror Detection.

Mirror detection is a challenging task since mirrors do not possess a consistent visual appearance. Even the Segment Anything Model (SAM), which boasts superior zero-shot performance, cannot accurately detect the position of mirrors. Existing methods determine the position of the mirror under hypothetical conditions, such as the correspondence between objects inside and outside the mirror, and the semantic association between the mirror and surrounding objects. However, these assumptions do not apply to all scenarios. For instance, there may be no corresponding real objects to the reflected objects in the scene, or it may be challenging to extract meaningful semantic associations in complex scenes. On the other hand, humans can easily recognize mirrors through the specular texture caused by materials. To mine mirror features in more general scenes, we propose a Cross-Space-Frequency Window Transformer (CSFwinformer) to extract spatial and frequency features for texture analysis. Specifically, we design a Spatial-Frequency Window Alignment module (SFWA) to calculate spatial-frequency feature affinities and learn the difference between mirror and non-mirror textures. We then propose a Dilated Window Attention (DWA) to extract global features to complement the limitation of window alignment. Besides, we propose a Cross-Modality Context Contrast module (CMCC) to fuse cross-modality features and global features, which enables information flow between different windows to take full advantage of cross-modality information. Extensive experiments show that our method performs favorably against state-of-the-art methods on three mirror detection benchmarks and significantly improved SAM performance on mirror detection. The code is available at https://github.com/wangsen99/CSFwinformer.

Research on Text Analysis Methods in English Translation Teaching Based on Semantic Association Network Modeling

Abstract As the mainstream language of the world, English still occupies an unshakeable prime position, and English translation is one of the essential learning content for students. The semantic network association network model is introduced in this paper, and a text analysis model for English translation is proposed. The model obtains word vectors through the GloVe word embedding model, combines the relevance measure of the Jaccard index, and applies the improved RelArtNet algorithm to calculate semantic relevance in English texts. The translated data from a prominent English test is selected as a sample to analyze the relevance of each feature value of English translation scores. From the three aspects of word surface, conceptual fidelity, and semantic expression, the textual analysis of the experimental corpus by the model yields the comprehensive scoring effect of English translation by the model. The correlation between translation length and English translation scoring is the highest, and the correlation of scoring in both English to Chinese and Chinese to English is more significant than 0.58. The overall model scoring and manual scoring of English translation are closer, with a difference of 1.59 in scoring and a correlation coefficient of greater than 0.8, which verifies the practical value of the model for scoring English translations. The English translation scoring model can help teachers review translations and students practice independently, which has important practical significance.

Growable design of passenger vehicle interior space based on FAHP and FQFD.

The increasingly shortened development cycle of smart vehicles has led to a qualitative shift in the nature of automotive products. Growing spatial design of vehicle interiors can effectively satisfy users' personalisation preferences and increase their willingness to buy, as well as mitigating the environmental pollution caused by the problem of rapid replacement. Considering the subjectivity and uncertainty of users' emotional needs, this study adopts the FAHP method to comprehensively analyse and rank the SET series of factors, then combines the grey correlation method with the correlation analysis of the areas related to the interior space of the automobile, constructs the sample of the interior space of the automobile and extracts the kansei words of the space sample. Intentional vocabulary mean scores were calculated to factor analyses through kansei engineering, next the fuzzy QFD quality house was built to make affective semantic design associations and derive design weights, which are then used to guide the design and ultimately realise the design of a dynamic automotive interaction scenario. The results of the study show that the integration of different theories can reduce the uncertainties in accessing users' emotional needs. At the same time, it can provide systematic guidance for the interaction design of a growable automobile in terms of multiple dimensions of interior space connectivity, spatial layout, and perceptual experience, as well as provide valuable suggestions for the subsequent development of interior spaces.

Interactive Subspace Cluster Analysis Guided by Semantic Attribute Associations.

Multivariate datasets with many variables are increasingly common in many application areas. Most methods approach multivariate data from a singular perspective. Subspace analysis techniques, on the other hand. provide the user a set of subspaces which can be used to view the data from multiple perspectives. However, many subspace analysis methods produce a huge amount of subspaces, a number of which are usually redundant. The enormity of the number of subspaces can be overwhelming to analysts, making it difficult for them to find informative patterns in the data. In this article, we propose a new paradigm that constructs semantically consistent subspaces. These subspaces can then be expanded into more general subspaces by ways of conventional techniques. Our framework uses the labels/meta-data of a dataset to learn the semantic meanings and associations of the attributes. We employ a neural network to learn a semantic word embedding of the attributes and then divide this attribute space into semantically consistent subspaces. The user is provided with a visual analytics interface that guides the analysis process. We show via various examples that these semantic subspaces can help organize the data and guide the user in finding interesting patterns in the dataset.

Lexical Characterization of Ancient and Modern Chinese Combined with Semantic Association Network Modeling

Abstract This paper explores the linguistic features of ancient and modern Chinese, focuses on capturing the knowledge of the relevant domains of ancient and contemporary Chinese using semantic models, and carries out the corresponding semantic resource extraction and semantic representation through the resource expression mechanism in the framework of the Semantic Association Network Model (SANM), and then analyzes the historical evolution of the lexicalization patterns of the Chinese language and the linguistic features of the Chinese language in different periods. In the historical evolution of Chinese lexicalization patterns, it is found that the distribution of lexicalization patterns of ancient Chinese table-cutting action lexical items is [action+object] > [action+method] > [action result] > [action+tool], and the distribution of lexicalization patterns of modern Chinese table-cutting action lexical items is [action+method] > [action+tool] > [action+result] > [action+object]. The linguistic features of different periods show that all networks have small-world properties and scale-free properties, and the statistical parameters of the networks in each period are relatively close to each other, with the modern network having the most significant C (8.59) and the smallest L (0.92) and γ (0.40). Ancient and modern Chinese words and phrases with relational word collocations in the interval [0,23] have the most extensive and densest distribution of relational words. These data present the similarities and differences between linguistic features between ancient and modern Chinese, which is significant for studying ancient and modern Chinese.

Toward Video Anomaly Retrieval From Video Anomaly Detection: New Benchmarks and Model.

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., "vandalism", is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks and design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method. Captions are publicly released at https://github.com/Roc-Ng/VAR.

Study on personalised search of English teaching resources database based on semantic association mining

Study on personalised search of English teaching resources database based on semantic association mining

Research on Online Interactive Teaching Platform of College English Combined with Semantic Association Network Modeling

Abstract The emergence of semantic association networks has injected a new impetus for the development of online English teaching and provided a new model reference for the design of online education platforms. In this paper, the research and design of an online interactive teaching platform for college English draws on the algorithmic advantages of the semantic associative network model and utilizes the self-operation of the semantic associative network to realize the functions of autonomous addition, deletion, modification, and checking. The text semantic similarity is predicted by word embedding model, convolutional neural network, and other algorithms so as to better achieve the integration of teaching resources, connecting English knowledge and highlighting the teaching focus in the online teaching process of college English. Dynamic load balancing algorithms are used to solve the problems of short-term surges in the number of visits and the concentration of call requests, and the optimization of load balancing algorithms is further realized through genetic algorithms to finally complete the design of the online teaching interactive platform. Comparison experiments concluded that the semantic association network proposed in this paper could hold a more stable repair effect when cleaning inconsistent data in the dataset, highlighting the effectiveness of the semantic association network model in this paper. The online interactive teaching platform designed in this paper also performs well in the performance test, with only a 0.01% abnormality rate in the concurrency performance test, and the load balancing ability test also achieves the expected effect.

Digital assessment of cognition in neurodegenerative disease: a data driven approach leveraging artificial intelligence.

A rapid and reliable neuropsychological protocol is essential for the efficient assessment of neurocognitive constructs related to emergent neurodegenerative diseases. We developed an AI-assisted, digitally administered/scored neuropsychological protocol that can be remotely administered in ~10 min. This protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illnesses. The protocol was administered to 77 ambulatory care/memory clinic patients (56.40% women; 88.50% Caucasian). The protocol includes a 6-word version of the Philadelphia (repeatable) Verbal Learning Test [P(r)VLT], three trials of 5 digits backward from the Backwards Digit Span Test (BDST), and the "animal" fluency test. The protocol provides a comprehensive set of traditional "core" measures that are typically obtained through paper-and-pencil tests (i.e., serial list learning, immediate and delayed free recall, recognition hits, percent correct serial order backward digit span, and "animal" fluency output). Additionally, the protocol includes variables that quantify errors and detail the processes used in administering the tests. It also features two separate, norm-referenced summary scores specifically designed to measure executive control and memory. Using four core measures, we used cluster analysis to classify participants into four groups: cognitively unimpaired (CU; n = 23), amnestic mild cognitive impairment (MCI; n = 17), dysexecutive MCI (n = 23), and dementia (n = 14). Subsequent analyses of error and process variables operationally defined key features of amnesia (i.e., rapid forgetting, extra-list intrusions, profligate responding to recognition foils); key features underlying reduced executive abilities (i.e., BDST items and dysexecutive errors); and the strength of the semantic association between successive responses on the "animal" fluency test. Executive and memory index scores effectively distinguished between all four groups. There was over 90% agreement between how cluster analysis of digitally obtained measures classified patients compared to classification using a traditional comprehensive neuropsychological protocol. The correlations between digitally obtained outcome variables and analogous paper/pencil measures were robust. The digitally administered protocol demonstrated a capacity to identify patterns of impaired performance and classification similar to those observed with standard paper/pencil neuropsychological tests. The inclusion of both core measures and detailed error/process variables suggests that this protocol can detect subtle, nuanced signs of early emergent neurodegenerative illness efficiently and comprehensively.

Semiotic Interpretation of Literary Metaphors in Henry James’ Psychological Fiction under the Semantic Association Network Modeling

Abstract Henry James enriched the representation of realism with the aesthetic practice of psychological realism theory, and his literary metaphors revealed the colorfulness of the human consciousness field. In this paper, under the background of semiotics, based on two algorithms, Semantic Association Network, Associative Component Analysis, and Dependent Syntax, we constructed a tensor representation model of literary text, extracted the symbolic information of sentences in the scholarly text, and combined the information of textual words through the association coefficients for the tensor representation of sentence metaphors. Quantitatively calculating the metaphoric semantics in the extracted literary sentences is followed by labeling and recognition of the literary sentences based on the metaphoric information. Take Henry James’, the Aristocats as an example and interpret the symbolic information in it using the model constructed in this paper. The symbolic scene of the “garden” in the novel represents the meaning of innocence and simplicity, which has the highest conceptual similarity enhancement of 3.415. Most of the emotions behind the readers’ evaluations of Henry James’s novels have positive metaphors, and the favorable rating range is mainly concentrated in the range of 0-100. In Henry James’s novels, the emotional metaphor of “repression” is more characteristic than other metaphors, with a maximum of 1.896. This paper investigates the interpretation of metaphors in Henry James’s novels, which can assist in the comprehension of the books.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Abstract Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.

Дискурсивні характеристики комунікантів модифікованих паремій

The article is devoted to the study of the discursive characteristics of communicators (both the addresser and the addressee) of occasionally modified paremias in modern German-language media discourse. Modified paremias are seen as the result of adaptation of traditional paremias to the features of socio-cultural re-evaluation of social norms and stereotypes, «speech democratization». In the study, it is noted that the modification of paremias is communicatively productive due to the text-forming features of traditional paremias, which serve as templates for new attitudes and semantic associations creation. The inclusion of modifications in the discourse is carried out by the addresser taking into account cognitive and communicative characteristics of the addressee. It was determined that occasionally modified paremias, on the one hand, preserve the established structural and semantic characteristics of traditional paremias, and on the other hand, introduce new communicative meanings and intentions that may correlate or contrast with the original source. It has been suggested that the addresser exerts a dual emotional and intellectual influence on the addressee by means of preserving conceptual implications of traditional paremia, and declaring a subjective vector of cognitive interpretation of information, as well as the type of interpersonal relations with the addressee. It has been concluded that in the aspect of interpersonal relations between communicators, priority is given to social statuses. The dual nature of the addresser’s social status is decisive, as a synergy of the borrowed status of traditional paremia and their personal status. The success of speech cooperation is ensured by interest, communicative identity in terms of background and propositional knowledge, interdependence of the addresser and the addressee. The addresser softens the socially determined authority of traditional paremia for the sake of establishing contact, a certain intimacy, dialogization and simplification of communication with the addressee.

THE FIELD APPROACH TO THE IDENTIFICATION AND EXAMINATION OF SEMANTIC GROUPS OF LANGUAGE UNITS (BASED ON THE MATERIAL OF THE GROUP “TOYS FOR CHILDREN” IN THE ENGLISH LANGUAGE)

Statement of the problem. The article considers various criteria for identifying groupings of linguistic units and the possibility of attributing a particular grouping to a certain type of semantic association of linguistic units. The objectives of the research are to describe the methods and the process of identifying semantic groupings of linguistic units, taking into account their various characteristics; to use the field method for identifying and describing the semantic field (SF) “Toys for children” in the English language and to define its boundaries. Results. The types of field structures and criteria for their differentiation are given. The methods of distinguishing lexical groupings and the fundamentals of the field theory are listed. The criteria to attribute the grouping of linguistic units naming children's toys to the semantic field are given. Dictionaries and websites of British and American stores containing catalogs of toys, as well as online platforms for the sale of children's toys serve as the sources of the research material. The quantity and quality of the material makes it possible to identify the SF “Toys for children" and define its boundaries. Conclusion. The group of English-language units “Toys for children” is one of the groupings that is dynamically replenished and updated as a result of linguistic and extralinguistic factors. The linguistic factors of the grouping growth include the detailing of toy names (doll: fashion doll, paddle doll, Matryoshka doll), the appearance of synonyms (Matryoshka doll, stacking dolls, nesting dolls, Russian tea dolls, Russian dolls), hyponyms and hyperonyms (vehicle: truck, car, bulldozer, tractor). The SF “Toys for children" is an open grouping as a result of its constant replenishment with new units (Aquabeads, Nerf blaster, glowpad, Magna Doodle, Neocube, Orbeez, flashlight projector.).