Semantic Categories Research Articles

Niemieckie i polskie nazwy szkół wyższych w ujęciu języko- i przekładoznawczym (XX i XXI wiek)

GERMAN AND POLISH NAMES OF HIGHER SCHOOLS FROM A LINGUISTIC AND TRANSLATIONAL PERSPECTIVE (20TH AND 21ST CENTURIES) The present paper is an attempt to describe the nomination usus (namegiving linguistic custom) regarding higher schools of the German-speaking area on the one hand, and of the Polish-speaking one on the other. The specific semantic categories of nominal components correspond to grammatical realisations, which are subject to certain structural constraints, as well as exhibit more and less typical forms. Informed by such observations – initiated by Jan Iluk’s overview paper [2000] and refined by an analysis of much broader material, encompassing Austrian and Swiss names as well – I tried to show the name-giving possibilities and tendencies in this field in both German and Polish, mainly with a view to a translation-related application of such contrastive analysis. The findings presented here, based on the material of a considerable number of higher school names in the examined area and combined with the presentation of about 100 German and some 45 Polish examples (provided with translations), should help both in evaluating already published equivalents, hardly ever official in the case of German, and proposing one’s own ones – also because of the non-existence of given equivalents in publications, be it only Internet-based ones.

The lexical expression of CIRCUMSTANCE and DEGREE in old English

This paper explores the distribution of the affixal material and lexical meaning within the semantic categories ‘circumstance’ and ‘degree’. The analysis is confined to the predicates belonging to the four major lexical categories, namely verbs, nouns, adjectives, and adverbs, that have been formed either prefixally or suffixally. Special attention has been paid to affixal polysemy, which applies when a given form can be bound to more than one semantic rule. In this regard, it must be noted that both categories present a significant degree of affixal overlapping that is confirmed in the polysemous character of a group of affixes, which include for-, fore-, forð-, mid-, ofer-, on-, sin-, ūp- and ūt-. The analysis has also revealed that ‘circumstance’ is mainly characterized by affixes conveying location, whereas ‘degree’ is mainly defined in terms of affixes expressing intensity. On the whole, the study casts lights on the degree of compatibility among affixes and functions and on the patterns of recategorization that arise as a result of affixal processes.

The Pragmatic Motivation of Atypical English Way into/to Construction

The English way construction exhibits distinct constructional meaning and semantic features; however, the prepositional slot in this construction has not received sufficient attention, and there remains a lack of comparative research regarding different prepositions in this field. This study set out to examine the semantic classification, mapping features, cognitive factors in the subcategory English way into/to construction, and to improve the algorithm of ΔPratio. The research adopted a bottom-up corpus-driven approach based on the BNC and used HAC (Hierarchical Cluster Analysis) to explore the semantic categories of the verb collexemes and compare the lexical richness in these two constructions. The results revealed that at the level of linguistic structure, coercion and metaphor, as well as family resemblance, served as legitimization methods for verb entry into the construction, while the principles of economy and expressivity disclosed the intentions and motivations behind the semantic extensions of constructions at the pragmatic level. Additionally, this research found that the latter two principles were competitive in the interpretation process of way into/to construction, providing insights into understanding the psychological and social factors in language communication.

A corpus‐based analysis of adjective amplification in Hong Kong, Indian and Philippine English

AbstractThis study considers a corpus‐based approach for analysing adjective amplifier systems (very, really, so, etc.) in Hong Kong English (HKE), Indian Englsih (IndE) and Philippine English (PhiE) based on data from the International Corpus of English. The current study adds to existing research by providing insights into the understudied adjective amplifier systems of Asian Englishes. The study describes the amplifier systems in HKE, IndE and PhiE, and it ascertains if these amplifier systems are undergoing change. In the latter context, the analysis assesses if really or so is replacing very as the dominant adjective amplifier in these varieties—a trend shown to be at work in Inner Circle varieties of English. The results of Conditional Inference Trees show that the amplifier systems of HKE and IndE are very stable and amplifier choice in these varieties is determined predominately by intra‐linguistics factors (adjective type, syntactic context and semantic category). In contrast, the amplifier system of PhiE shows notable signs of ongoing change, which is driven predominately by social factors (age and gender of speakers). The results indicate that during stasis and initial stages of change, language‐internal factors determine amplifier choice, while social factors become more important once changes have reached the midrange. The paper argues that once certain variants (really and so) gain social meaning, it is this social meaning (the association with specific social groups) that drives and accelerates change as speakers want to associate with social groups that carry covert prestige.

What makes a word a good representative of the category of "emotion"? The role of feelings and interoception.

The words we use to describe emotions vary in terms of prototypicality; that is, some of these words may be more representative of the semantic category of emotion than others (e.g., anger refers more clearly to an emotion than boredom). Based on a multicomponential conception of emotions, the aim of the present study was to examine the contribution of several variables to emotion prototypicality. Some of those variables are related to the distinct components of emotions: evaluation, action, body expression, internal body sensations (interoception), and feelings. Other variables are related to the concreteness/abstractness distinction: sensory experience, social interaction, thought, and morality. We collected ratings for these variables for a large set of words (1,286) which varied in emotion prototypicality. A regression analysis revealed that the variables that most contributed to emotion prototypicality were feelings and interoception. Furthermore, a factor analysis identified two underlying factors: socioemotional polarity and emotional experience. The scores of each word in both factors were used to create a two-dimensional space and a density plot which provides relevant information about the organization of emotion concepts in memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Repeated naming affects the accessibility of nonselected words: Evidence from picture-word interference experiments.

This study traced different types of distractor effects in the picture-word interference (PWI) task across repeated naming. Starting point was a PWI study by Kurtz et al. (2018). It reported that naming a picture (e.g., of a duck) was slowed down by a distractor word phonologically related to an alternative picture name from a different taxonomic level ("birch" related to "bird") when compared to an unrelated control, indicating that the alternative name was (phonologically) coactivated. Importantly, the effect was stable across repeated naming. The authors argued that this stability challenges adaptive models of word production according to which coactivated but nonselected words become less accessible for future retrieval; such a change in lexical accessibility should have been reflected in reduced interference. Using a similar experimental protocol as Kurtz et al., our study looked at the stability of different distractor effects in PWI. Interference from a distractor word phonologically related to an alternative name was stable across repeated naming, replicating Kurtz et al. In contrast, interference from a distractor word denoting a semantic category coordinate ("stork") was reduced across repeated naming. A similar pattern was found for a distractor word corresponding to an alternative name ("bird"). Facilitation from a distractor word phonologically related to the target name ("dust"), in contrast, was stable across repeated naming. We discuss the implications of these findings with respect to the issue of changes in the accessibility of lexical representations, the use of the PWI task in this context, and the use of this task in general. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Transdiagnostic Analysis of Verbal Fluency across Autism Spectrum Disorder, Schizophrenia, and Neurotypical Healthy Control Groups

IntroductionVerbal fluency, a cognitive function that reflects executive functions and the rapid retrieval of pertinent information from memory, has yielded inconsistent findings in previous research on autism spectrum disorder (ASD), however in schizophrenia (SCH) semantic fluency exhibits a more pronounced impairment compared to letter fluency.ObjectivesIn this study we aim to comprehensively investigate verbal fluency in ASD, SCH, and neurotypical healthy control individuals (NTP). The primary objective is to investigate disparities in novel response generation, specifically between the ASD, SCH and NTP groups, using phonemic and semantic fluency tasks. Three central inquiries guide our research: (1) whether differences between groups (ASD, SCH, and NTP) can be identified in word productivity, clustering, errors, and perseverations; (2) whether participants with ASD and SCH exhibit different word production with elevated imageability and concreteness values; and (3) if individuals with ASD and schizophrenia demonstrate reduced productivity during the earlier phases of fluency tasks.MethodsForty participants with ASD (12 female, 24 male, 4 other, mean age: 30.5), 39 with SCH (10 female, 28 male, 1 other, mean age: 34.7) and 41 NTP (13 female, 28 male, mean age: 31.0) were recruited from the outpatient units of the Department of Psychiatry and Psychotherapy, Semmelweis University. Participants were requested to list as many words as they could on two phonemic and two semantic category conditions. Audio recordings were later transcribed. To assess concreteness and imageability, we employed a seven-point scale and recruited independent external raters to evaluate a total of 1481 words.ResultsPreliminary results indicate that the three study groups did not differ significantly in phonemic fluency (F(2, 119)= 0.983, p=0.377), during either time period. However, a significant difference was observed in semantic fluency (F(2, 119)= 6.531, p=0.002). Post-hoc tests (Tukey corrected) revealed that this difference stemmed from impaired performance in the SCH group. Participants with schizophrenia (SCH) exhibited reduced semantic word productivity compared to both neurotypical (NTP) individuals and participants with ASD (Figure 1). However, there were no significant differences between participants with ASD and NTP individuals.Image:ConclusionsIn conclusion, our study investigated the characteristics of verbal fluency in a transdiagnostic approach. While phonemic fluency did not reveal significant differences among the three groups, our analysis of semantic fluency unveiled a distinction. Specifically, individuals with schizophrenia exhibited impaired semantic word productivity. Our study highlights the complex nature of verbal fluency impairments in different conditions and the importance of considering more nuanced methods when assessing cognitive functions.Disclosure of InterestNone Declared

Behavioral and neural measures of semantic conflict monitoring: Findings from a novel picture-word interference task

Conflict monitoring has been studied extensively using experimental paradigms that manipulate perceptual dimensions of stimuli and responses. The picture-word interference (PWI) task has historically been used to examine semantic conflict, but primarily for the purpose of examining lexical retrieval. In this study, we utilized two novel PWI tasks to assess conflict monitoring in the context of semantic conflict. Participants included nineteen young adults (14F, age = 20.79 ± 3.14) who completed two tasks: Animals and Objects. Task and conflict effects were assessed by examining behavioral (reaction time and accuracy) and neurophysiological (oscillations in theta, alpha, and beta band) measures. Results revealed conflict effects within both tasks, but the pattern of findings differed across the two semantic categories. Participants were slower to respond to unmatched versus matched trials on the Objects task only and were less accurate responding to matched versus unmatched trials in the Animals task only. We also observed task differences, with participants responding more accurately on conflict trials for Animals compared to Objects. Differences in neural oscillations were observed, including between-task differences in low beta oscillations and within-task differences in theta, alpha, and low beta. We also observed significant correlations between task performance and standard measures of cognitive control. This work provides new insights into conflict monitoring, highlighting the importance of examining conflict across different semantic categories, especially in the context of animacy. The findings serve as a benchmark to assess conflict monitoring using PWI tasks across populations of varying cognitive ability.

Semantic facilitation in blocked picture categorization: Some data and considerations regarding task selection.

Semantic context effects in picture naming and categorization tasks are central to the development and evaluation of current models of word production. When pictures are named in a semantically blocked context, response latencies are delayed. Belke (2013) found that when the naming task was replaced with a semantic categorization task (natural vs. man-made), response latencies were facilitated. From this pattern, she concluded that semantic interference in blocked picture naming has its locus at the lexical level but its origin at the preceding semantic level. However, other studies using the blocking procedure have failed to find facilitation in semantic categorization tasks (Damian et al., 2001; Riley et al., 2015), calling this conclusion into question. In three blocked picture naming and categorization experiments, we investigated different variables that might account for the discrepant results in semantic categorization. We used different semantic categorization tasks, different response modalities, different response set sizes, and different blocking procedures. Semantic facilitation was reliably found in naturalness categorization, but there was no semantic effect in natural size categorization. We discuss the implications of these findings for appropriate task selection. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

UniCell: Universal Cell Nucleus Classification via Prompt Learning

The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://github.com/lhaof/UniCell

Multi-Modal Prompting for Open-Vocabulary Video Visual Relationship Detection

Open-vocabulary video visual relationship detection aims to extend video visual relationship detection beyond annotated categories by detecting unseen relationships between objects in videos. Recent progresses in open-vocabulary perception, primarily driven by large-scale image-text pre-trained models like CLIP, have shown remarkable success in recognizing novel objects and semantic categories. However, directly applying CLIP-like models to video visual relationship detection encounters significant challenges due to the substantial gap between images and video object relationships. To address this challenge, we propose a multi-modal prompting method that adapts CLIP well to open-vocabulary video visual relationship detection by prompt-tuning on both visual representation and language input. Specifically, we enhance the image encoder of CLIP by using spatio-temporal visual prompting to capture spatio-temporal contexts, thereby making it suitable for object-level relationship representation in videos. Furthermore, we propose visual-guided language prompting to leverage CLIP's comprehensive semantic knowledge for discovering unseen relationship categories, thus facilitating recognizing novel video relationships. Extensive experiments on two public datasets, VidVRD and VidOR, demonstrate the effectiveness of our method, especially achieving a significant gain of nearly 10% in mAP on novel relationship categories on the VidVRD dataset.

Learning Content-Enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized semantic predictions across diverse urban-scene styles. Unlike generic domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions, lighting, and other factors. Existing approaches typically rely on convolutional neural networks (CNNs) to learn the content of urban scenes. In this paper, we propose a Content-enhanced Mask TransFormer (CMFormer) for domain-generalized USSS. The main idea is to enhance the focus of the fundamental component, the mask attention mechanism, in Transformer segmentation models on content information. We have observed through empirical analysis that a mask representation effectively captures pixel segments, albeit with reduced robustness to style variations. Conversely, its lower-resolution counterpart exhibits greater ability to accommodate style variations, while being less proficient in representing pixel segments. To harness the synergistic attributes of these two approaches, we introduce a novel content-enhanced mask attention mechanism. It learns mask queries from both the image feature and its down-sampled counterpart, aiming to simultaneously encapsulate the content and address stylistic variations. These features are fused into a Transformer decoder and integrated into a multi-resolution content-enhanced mask attention learning scheme. Extensive experiments conducted on various domain-generalized urban-scene segmentation datasets demonstrate that the proposed CMFormer significantly outperforms existing CNN-based methods by up to 14.0% mIoU and the contemporary HGFormer by up to 1.7% mIoU. The source code is publicly available at https://github.com/BiQiWHU/CMFormer.

The meaning of morphomes: distributional semantics of Spanish stem alternations

Abstract Romance stem alternations have been argued to represent exclusively morphological objects (or “morphomes”) independent from semantic and syntactic categories. This conclusion has been based on feature-value analyses of the inflected forms, and definitions of natural classes that are theoretically driven and about which no consensus exists. Individual examples of morphomes are thus frequently challenged, while their autonomously morphological nature has never been tested quantitatively or experimentally. This is the purpose of the present study. We use context-based embeddings to explore the semantic profile of Spanish verb stem alternations. At the paradigmatic level, our findings suggest that Spanish morphomes’ cells are characterized by significantly above-chance distributional-semantic similarity. At the lexical level, similarly, verbs that show more similar patterns of alternation have also been found to be closer in meaning. Both of these findings suggest that these structures may have an extramorphological function. Using gradient distributional-semantic similarity offers a way to objectively assess the degree of (un)naturalness of a set of forms and meanings, something which has been lacking from most discussions on the structure of features and the architecture of paradigms.

CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements.

Film discourse: the semantic interpretation of film titles

This article discusses the problems of semantic interpretation of the titles of feature films, as films turned into influential means of mass communication in the modern life. Filmonyms as one of the types of small-format texts has become the object of linguistic research. The significance of the film discourse as an audiovisual discourse is determined by the possibilities of its impact on the addressee. Author comprehensively analyzed the works of foreign and Kazakhstani linguists who studied the film discourse from various aspects. The purpose of the article is to identify linguistic and pragmalinguistic interpretation of Kazakhstani film titles. To achieve the goal, the following tasks were set: to consider semantic interpretation of the titles of feature films, to identify aesthetic function of film discourse, to group films into semantic categories, to offer the definition of the concept of “film discourse”. The scientific significance of the research work expands the research scope of film-discourse as a type of institutional discourse. Practical significance is due to the fact that research results can be used as materials for preparing lectures and cases on audiovisual discourse and pragmalinguistic features applied to the names of Kazakhstani films should undoubtedly become the object of further research by linguists.

Multimodal Knowledge-enhanced Interactive Network with Mixed Contrastive Learning for Emotion Recognition in Conversation

Emotion Recognition in Conversations (ERC) aims to accurately identify the emotional labels of each utterance in a conversation, holding significant application value in human–computer interaction. Existing research suggests introducing commonsense knowledge (CSK) and multimodal information enhances model performance in ERC tasks. However, several challenges persist: (1) the neglect of complex psychological influences between utterances; (2) noise issues within modal information; (3) prediction challenges for emotion labels with few samples in different categories that exhibit semantic similarity but distinct emotional categories. To address the above problems, we propose a Multimodal Knowledge-enhanced Interactive Network with Mixed Contrastive Learning (MKIN-MCL). Firstly, we establish a knowledge aggregation graph to capture the dependencies of commonsense knowledge (CSK) between utterances during a conversation. We actively aggregate relevant knowledge information to enhance text features. Simultaneously, we apply feature filters for acoustic and visual modalities to eliminate noise and enhance feature quality. Furthermore, we implement an interactive attention module by stacking designed Cross-modal Interactive Transformers (CITs) to continuously explore the relevance between the interacting parties in their respective semantic spaces, thus improving the effectiveness of modality interaction while reducing noise generated during the interaction. Lastly, we employ the Mixed Contrastive Learning (MCL) strategy to enhance the model’s ability to handle few-shot labels. This strategy utilizes unsupervised contrastive learning to improve the representation capability of the multimodal fusion features and supervised contrastive learning to extract information from few-shot labels. Extensive experiments on two benchmark datasets, IEMOCAP and MELD, validate the effectiveness and superiority of the proposed model.

Superpixel-wise contrast exploration for salient object detection

Salient object detection (SOD) methods typically consider SOD as a pixel-wise binary classification problem and utilize the binary cross-entropy (BCE) loss for optimization. However, the BCE loss ignores the global dependencies between pixels of the entire image, which is important for ensuring the accuracy and integrity of objects. To address this limitation explicitly, contrastive learning is introduced to enhance both the intra-pixel compactness of the foreground and inter-pixel separability between the foreground and background. Unfortunately, the random pixel sampling strategies of existing contrast algorithms cannot ensure that the sampled pixels cover all semantic contents across the entire image because of the lack of fine-grained semantic categories for pixels in SOD. Hence, in this study, a SuperPixel-wise Contrastive (SPCont) algorithm is proposed, which samples all superpixel centers to improve the diversity and representativeness of the sampled pixels. In addition, a SOD-specific hard pixel sampling strategy is designed to refine the edge details of the objects. An intra-image multi-level negative sampling approach is presented to increase the number of negative samples. Extensive experiments are conducted to demonstrate that incorporating the SPCont algorithm into the state-of-the-art network results in significant performance improvements. We expect that our work will open new paths for the application of contrastive learning to SOD.

Predicting anxiety using Google and Youtube digital traces

Anxiety is a widespread and serious mental health issue that has been exacerbated by the COVID-19 pandemic and other stressors. In this study, we explore how online behavior data from Google and YouTube can be used to infer anxiety levels in individuals. We collected and processed digital traces from nearly 100 participants over eight weeks and applied various machine learning techniques to extract features and build predictive models. We found that combining data from multiple media modalities can yield highly accurate predictive models for anxiety as self-reported by a clinical GAD-7 scale (AUC > 0.86). We also found that the semantic categories of online engagement can affect the predictive performance of the models. This study contributes to the field of computational social science and digital mental health and demonstrates the potential of using online behavior data to monitor psychological well-being and design interventions for anxiety.

Development of a vocabulary list for the Persian version of the profile of early expressive phonological skills: a pilot study

ABSTRACT This research presents a two-phase study aimed at developing and validating the Profile of Early Expressive Phonological Skills for Persian (PEEPS-P) as an assessment tool for evaluating phonological abilities in toddlers. In Phase 1, a total of 60 words were meticulously selected for inclusion in both the basic and expanded word lists, taking into account factors such as age of acquisition (AoA) and phonological complexity. The word lists included monosyllabic and multisyllabic words with diverse consonant clusters, covering various semantic categories. Moving on to Phase 2, data were collected from a sample of 81 toddlers belonging to different age groups. Three elicitation methods were employed to gather data for both the basic and expanded word lists. The study also examined the time required to complete the test and Percentage of Consonant Correct (PCC) scores. The results revealed that, as children's age increased, there was an increase in the percentage of words produced, accompanied by a decrease in the time taken to complete the test. For instance, the youngest age group (18–23 months) produced approximately 46.83% of words in the basic word list, taking an average of about 22 minutes and 3 seconds, while the oldest age group (30–35 months) produced approximately 95.5% of words in both word lists in less than 17 minutes. Ultimately, the findings suggest that the PEEPS-P has the potential to serve as a valuable tool for assessing phonological abilities in toddlers aged 18–35 months.

Taxis trough the prism of the categorial interaction (based on the modern German)

The focus of this article is the speech actualization of taxis categorial meanings of simultaneity and non-simultaneity in German statements with temporal-taxis prepositions. The purpose of the study is to determine the process of taxis actualization as a result of intercategorial connections and interactions of semantic categories representing the idea of time in modern German. The subject of this study is the issue of taxis actualization in the German language, considered in the context of the implementation of intercategorial relations of taxis, temporality and aspectuality. The material of this study is German utterances with actional deverbatives die Trennung, die Scheidung and temporal-taxis prepositions bei, mit, seit, bis, nach, vor, obtained by directed sampling from the electronic database of the Leipzig Corpus (LC). In the course of the study, it was determined that the intercategorial interaction of taxis with the semantic categories of aspectuality and temporality determines the actualization of various primary taxis categorial situations of simultaneity, precedence and succession. These are temporal-taxis, durative-taxis, iterative-taxis, phase-taxis, and other categorical values. As a result of the study, it was revealed that the segment of the zone of intersection of the categories of taxis, temporality and aspectuality is constituted by temporal-taxis prepositions, temporal adverbials and aspectual quantifiers. The latter include durative, iterative and phase adverbials and attributes. They function as indicators of conjugated primary taxis categorial situations.