Semantic Categories Research Articles

HISTORICAL FORMATION OF PRONOUNS IN UZBEK LANGUAGE

Periodic study of pronouns in the Uzbek language, comparing their forms in ancient written monuments with their modern forms, is one of the main goals of our topic. In the article, the phonetic, semantic,lexical meanings, number and agreement categories of the reflexive pronoun and its forms and characteristics in ancient Turkic language monuments, its function in the sentence were studied, compared and analyzed through examples. Changes in the historicaldevelopment of each language primarily affect its lexicon and vocabulary. We focus on pronouns because despite constituting only a tiny fraction of the available words in the English language, they are among the most frequently used (Pennebaker, 2011). Furthermore, extensive research has established that pronouns play a fundamental role in people’s everyday experiences.

Irrealis expressions and modality: A response to von Prince, Krajinović, and Krifka

Abstract: Von Prince, Krajinović, and Krifka (2022) argue that irrealis is a crosslinguistically legitimate semantic category, and they define it in terms of a domain encompassing both future possibility and counterfactuality. In this response, we argue that this definition is too narrow, because it excludes past and present possibility and necessity. We suggest instead that the correct characterization is that irrealis expressions correlate with quantification over possible worlds—or in simpler terms, with modality. We then ask a compositional question: do irrealis expressions signal the presence of modality contributed by other morphemes in the clause, or do they contribute modality themselves? Based on a comparison between the languages in von Prince et al.’s sample and preliminary data from Lutuv (Lautu) Chin (South Central Tibeto-Burman, formerly called Kuki-Chin), we suggest that the answer to this question may vary from one language to the next, thereby contributing to a richer picture of how modal meaning is reflected and encoded crosslinguistically.

Using drawings and deep neural networks to characterize the building blocks of human visual similarity.

Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perception. Contrasting five contemporary DNNs, we evaluated how well each explains human similarity judgments among line drawings of recognizable and novel objects. For object sketches, human judgments were dominated by semantic category information; DNN representations contributed little additional information. In contrast, such features explained significant unique variance perceived similarity of abstract drawings. In both cases, a vision transformer trained to blend representations of images and their natural language descriptions showed the greatest ability to explain human perceptual similarity-an observation consistent with contemporary views of semantic representation and processing in the human mind and brain. Together, the results suggest that the building blocks of visual similarity may arise within systems that learn to use visual information, not for specific classification, but in service of generating semantic representations of objects.

Learning robust correlation with foundation model for weakly-supervised few-shot segmentation

Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large number of pixel masks during training is expensive. This paper considers a more challenging scenario, weakly-supervised few-shot segmentation (WS-FSS), which only provides category (i.e. image-level) labels. It requires the model to learn robust support-query information when the generated mask is inaccurate. In this work, we design a Correlation Enhancement Network (CORENet) with foundation model, which utilizes multi-information guidance to learn robust correlation. Specifically, correlation-guided transformer (CGT) utilizes self-supervised ViT tokens to learn robust correlation from both local and global perspectives. From the perspective of semantic categories, the class-guided module (CGM) guides the model to locate valuable correlations through the pre-trained CLIP. Finally, the embedding-guided module (EGM) implicitly guides the model to supplement the inevitable information loss during the correlation learning by the original appearance embedding and finally generates the query mask. Extensive experiments on PASCAL-5i and COCO-20i have shown that CORENet exhibits excellent performance compared to existing methods.

Does targeted memory reactivation during slow-wave sleep and rapid eye movement sleep have differential effects on mnemonic discrimination and generalization?

Targeted memory reactivation (TMR), or the presentation of learning-related cues during sleep, has been shown to benefit memory consolidation for specific memory traces when applied during non-rapid eye movement (NREM) sleep. Prior studies suggest that TMR during rapid eye movement (REM) sleep may play a role in memory generalization processes, but evidence remains scarce. We tested the hypothesis that TMR exerts a differential effect on distinct mnemonic processes as a function of the sleep state (REM vs. NREM) in which TMR is delivered. Mnemonic discrimination and generalization of semantic categories were investigated using an adapted version of the Mnemonic Similarity Task, before and after sleep. Forty-eight participants encoded pictures from eight semantic categories, each associated with a sound. In the pre-sleep immediate test, they had to discriminate "old" (targets) from "similar" (lures) or "new" (foils) pictures. During sleep, half of the sounds were replayed in slow wave sleep (SWS) or REM sleep. Recognition, discrimination, and generalization memory indices were tested in the morning. These indices did not differ between SWS and REM TMR groups or reactivated and non-reactivated item categories. Additional results suggest a positive effect of TMR on performance for highly similar items mostly relying on mnemonic discrimination processes. During sleep, EEG activity after cue presentation increased in the delta-theta and sigma band in the SWS group, and in the beta band in the REM TMR group. These results do not support the hypothesis of differential processing of novel memory traces when TMR is administered in distinctive physiological sleep states.

Обработка зрительной вербальной информации в условиях чтения и категоризации слов в составе одного согласованного словосочетания

The purpose of this study was to identify neurophysiological differences in information processing by the brain during passive reading and semantic categorization of individual words within a phrase. Materials and methods. The research involved 25 natural science students of Nizhny Novgorod universities. In the course of the experiment, the subjects were presented with word combinations consisting of an attribute and a noun on a computer monitor. In the first series of presentations, the task was to classify the attribute (target condition) and read the noun (non-target condition), in the second, to classify the noun and read the attribute. This allowed us to assess the restructuring of information processing when changing the task within a phrase, as well as to compare the processing of the word in the first and second positions in the target and non-target conditions. Using the 21-channel Neuron-Spectrum-4/VPM electroencephalograph (Neurosoft, Russia), event-related potentials (ERPs) obtained during the response to presented stimuli were recorded. Results. No differences were observed in attribute processing between the target and non-target conditions when tasks changed. For nouns, differences were revealed in ERP components between the target and non-target conditions. There was a more pronounced peak of the P200 component, a less pronounced peak of the N400 and a late negative wave of the N700 component during the categorization of nouns compared to their passive reading. Thus, when performing different tasks within a phrase, the brain changes the nature of verbal information processing, which is manifested in strengthening early lexical access, followed by the facilitation of lexico-semantic processing and activation of cognitive control mechanisms at the latest stages during the categorization of the second word in a phrase (noun) compared to its passive reading.

Myth of mankind and the representation of people in Late 18th-century British dictionaries of trade and commerce

The investigation aims at analysing the two general notions of mankind and people in two lexicographic reference works on trade and commerce published in London in the 1750s: Rolt’s A New Dictionary of Trade and Commerce (1756) and Postlethwayt’s The Universal Dictionary of Trade and Commerce (1757). An overview of the same notions in universal dictionaries of arts and sciences (cfr. References, Primary Sources), issued before and after ND and UD, will also be of help to define a more general background. Starting from the key words mankind and people, their frequency and relevance in the two dictionaries, the investigation is widened to include related terms (e.g. multiword expressions and language patterns in which mankind and people occur) and semantic categories (e.g. specific contexts of use: social condition, social values, demography, identity and citizenship, etc.). The many entries, cross-references, and the various lexical clusters reveal a complex lexicographic and lexicological network. This network, beyond providing information and contents on specific topics, ultimately emphasises the hierarchical organising principle at the basis of Late Modern British society: in particular, people unfolds into a myriad of ‘entities’. Dictionaries of trade and commerce are plentiful sources of knowledge, from general concepts and notions to more specific contents, but also powerful ideological tools.

Saliency Detection Based on Multiple-Level Feature Learning.

Finding the most interesting areas of an image is the aim of saliency detection. Conventional methods based on low-level features rely on biological cues like texture and color. These methods, however, have trouble with processing complicated or low-contrast images. In this paper, we introduce a deep neural network-based saliency detection method. First, using semantic segmentation, we construct a pixel-level model that gives each pixel a saliency value depending on its semantic category. Next, we create a region feature model by combining both hand-crafted and deep features, which extracts and fuses the local and global information of each superpixel region. Third, we combine the results from the previous two steps, along with the over-segmented superpixel images and the original images, to construct a multi-level feature model. We feed the model into a deep convolutional network, which generates the final saliency map by learning to integrate the macro and micro information based on the pixels and superpixels. We assess our method on five benchmark datasets and contrast it against 14 state-of-the-art saliency detection algorithms. According to the experimental results, our method performs better than the other methods in terms of F-measure, precision, recall, and runtime. Additionally, we analyze the limitations of our method and propose potential future developments.

의미 운율의 정도성 연구

This paper aims to explore the need for expanding the scope and methodology of semantic prosody analysis. First, the scope of the co-occurrence relation of the node was expanded to include not only adverbs, but also subjects and words that modify them. The ‘category’ of the evaluative meaning of co-occurring words was analyzed by considering the context, and the ‘degree’ was measured using the KNU Korean Sentiment Lexicon. And the realization type of semantic prosody in the node was compared and analyzed by dividing it into type and co-occurrence relation. The results of the categorical and degree-based approaches to semantic prosody were compared. As a result of the analysis, first, the type of semantic prosody realization and usage patterns of co-occurring words were different depending on the node. Second, the usage patterns of subjects and adverbs were most similar to ‘giinhada-birotada’ and ‘birotada-oda’. Third, nodes with the same semantic prosody category could be compared more precisely by analyzing the degree of semantic prosody. This showed that there is a need to expand the scope of co-occurrence relation and methodology of analysis when analyzing semantic prosody.

A Literature Review of Language Characteristics in Patients with Vascular Dementia

Vascular dementia may present with language disorders due to damage to frontal subcortical circuits. This study aimed to review recent studies of language abilities in patients with vascular dementia. Sixty-nine studies were classified according to language modalities (spontaneous speech, auditory comprehension, repetition and naming). First, spontaneous speech was impaired in the ability to describe pictures, objects and in the ability to convey information in discourse. Second, the auditory comprehension was impaired in word comprehension, sentence comprehension, paragraph comprehension, and speech act comprehension tasks. Third, repetition was preserved in word-level tasks but impaired at the sentence level. Fourth, the naming performance was impaired in a generative naming task (phonemic fluency) rather than in a confrontational naming task. Therefore, when summarizing the results between patient groups, in spontaneous speech, to convey information through highly informative utterances was relatively good in vascular dementia (VaD), but the number of utterances was lower than dementia of Alzheimer’s type (DAT). In auditory comprehension, VaD’s performance was good in the task of word comprehension and speech act comprehension tasks. It is organized as in repetition, VaD’s performance was relatively good in the sentence-level task, and in naming, VaD’s performance was reported to be better than DAT in the oppositional naming and generative naming semantic category tasks. In the generative naming phoneme category, opposite result was reported. Further studies on the language abilities of patients with vascular dementia needs to consider factors such as subtype and language modality. Future studies should be evenly distributed across all language domains.

Design and Evaluation of English Vocabulary Learning Aids Based on Word Vector Modelling

English vocabulary learning aids based on word vector modeling involve creating tools that leverage advanced techniques to enhance vocabulary acquisition. Word vector modeling, often using methods like Word2Vec or GloVe, represents words as high-dimensional vectors capturing semantic relationships. These models can power vocabulary learning aids by offering context-based word suggestions, personalized word quizzes, or interactive visualizations of word associations.. The paper introduces the Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework, a novel approach aimed at enhancing vocabulary learning and classification tasks. With word vector modeling and spiking neural networks, HSV-DL offers a sophisticated methodology for accurately categorizing vocabulary words into their respective semantic categories. The Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework introduces novel methodologies for vocabulary learning and classification tasks, achieving outstanding performance metrics. Experimental results demonstrate high accuracy (95%), precision (96%), recall (94%), and F1-score (95%) in categorizing vocabulary words into their semantic categories. Moreover, HSV-DL exhibits robustness to noise and efficient resource utilization, showcasing its potential for real-world applications in natural language processing.

Artificial intelligence in judicial adjudication: Semantic biasness classification and identification in legal judgement (SBCILJ)

History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.

Determining a subset of discrete emotion-evoking images from the international affective picture system in a Turkish sample

The International Affective Picture System (IAPS), widely utilized in emotion research, is based on a dimensional approach. It includes 1196 colored static images depicting different semantic categories, with valence, arousal, and dominance ratings determined for each. However, the specific discrete emotions evoked by these images within the IAPS has remained unclear. Therefore, numerous investigations have been conducted in different cultural contexts to address this matter. This study aimed to determine a subset of images from the IAPS that elicit discrete emotions. To achieve this goal, an image was selected for each semantic category within the IAPS, employing specific criteria, and the elicited discrete emotions were subsequently examined in a Turkish sample. Additionally, valence ratings for these images were obtained within the Turkish culture, facilitating cross-cultural comparisons. Sixty Turkish students (43 female) aged between 19-25 participated in this study. The participants rated the valence, discrete emotion category, and the intensity of the determined discrete emotion (1 = none; 9 = very strongly) for 231 selected images from the IAPS, respectively. Valence ratings were obtained using the paper-pencil version of the Self-Assessment Manikin. Accordingly, 76 images, of which intensity ratings above six were classified into a single discrete emotion with the agreement of 70% and above among the participants. Furthermore, the obtained data were compared with the results of studies conducted in different cultural settings to explore potential cultural differences. Overall, the results highlighted the importance of selecting culture-specific stimuli in emotion studies.

Episodic long-term memory formation during slow-wave sleep.

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

Episodic long-term memory formation during slow-wave sleep

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words’ linguistic processing raised neural complexity. The words’ semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

On the Theoretically Possible Number of Cases in Natural Language

Introduction. The case, which is a semantic and grammatical category, appears in natural languages in the most unexpected way. A word or phrase that has a substantive unit at its core receives an inflection (internal or external) in one case or another, which can be expressed both explicitly and implicitly, and has a grammatical meaning. In this case, inflection can be accompanied by a preposition that has a lexical meaning. These case indicators, morphological and syntactic, organize the main or secondary substantive part of the sentence, i.e. are related to the category of space. The number of cases today is still the subject of scientific debate. Methodology and sources. The present study is based on analizing the views of various scientists on the category of case, with special attention to the theory of L. Hjelmslev. According to this theory, the maximum possible number of cases in natural language is 216. In order to provide a purely formal calculation of the possible number of cases, which would be at the same time independent on the earlier views, the article represents the substantive part of the sentence as a semifinitive multiplied by a specifier (proto-specifier). These statements are prescribed by the binomiality idea, having been developed since 1993 at ETU. Results and discussion. The generally accepted division of sentence parts into main and secondary ones leads us to dividing all possible cases into direct cases (for subject) and indirect cases (for secondary substantive parts). Thus, the direct case can be obtained by a simple transformation from any indirect case, i.e. the space specifier goes back to the spatial proto-specifier. The space proto-specifier represents external space added to substantive mass. The substantive semifinitive is a substantive mass multiplied by internal space. As the binomiality idea prescribes, substantive masses can be in four states, while substantive semifinitives can be in six states. Thus, the number of possible case forms should be equal to 24. Conclusion. According to classical physics, space is three-dimensional, so grammatical space, both external and internal, should also, obviously, be considered three-dimensional. This means that four states of substantive mass and six states of substantive semifinitives can be in each of the three dimensions. Thus, the maximum possible number of cases may indeed reach 216. It should be noted that L. Hjelmslev derived the number 216 in a completely different way, by dint of much deeper reasoning. In the future, it would be interesting to compare L. Hjelmslev’s binary oppositions with the constituents of substantive parts of sentence.

Category of learning modality in English texts

In the literature of linguistics, modality is a semantic category that expresses the attitude of the speaker to the content of his expression, the target designation of the speech, the relationship of the content of the expression with reality. The category of modality is a linguistic universal and stands out as one of the main categories of language. This article examines the category of modality, one of the most popular and interesting areas in modern English grammar. Different types of modality are defined, examples and features of working of modal words in English texts are given, detailed explanation is given to the presented usage examples. The relevance of the research is related to the necessity of a comprehensive review of the category of modality from the point of view of the theoretical grammar of the modern English language, the importance of systematizing modal words, and determining their status in linguistics.

Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% of the Earth's biodiversity. However, we are witnessing an average loss of 480 ha of forest every hour because of destructive wildfires spreading across the globe. To effectively mitigate the threat of wildfires, it is crucial to devise precise and dependable approaches for forecasting fire dynamics and formulating efficient fire management strategies, such as the utilisation of fuel models.The objective of this study was to enhance forest fuel classification that considers only structural information, such as the Prometheus model, by integrating data on the fire responses of various tree species and other vegetation elements, such as ground litter and shrubs. This distinction can be achieved using multispectral (MS) Light Detection and Ranging (LiDAR) data in mixed forests. The methodology involves a novel approach in semantic classifications of forests by generating synthetic data with semantic labels regarding fire responses and reflectance information at different spectral bands, as a real MS scanner device would detect. Forests, which are highly intricate environments, present challenges in accurately classifying point clouds. To address this complexity, a deep learning (DL) model for semantic classification was trained on synthetic point clouds in different formats to achieve the best performance when leveraging MS data.Forest plots in the study region were scanned using different Terrestrial Laser Scanning sensors at wavelengths of 905 and 1550 nm. Subsequently, an interpolation process was applied to generate the MS point clouds of each plot, and the trained DL model was applied to classify them. These classifications surpassed the average thresholds of 90% and 75% for accuracy and intersection over union, respectively, resulting in a more precise categorisation of fuel models based on the distinct responses of forest elements to fire. The results of this study reveal the potential of MS LiDAR data and DL classification models for improving fuel model retrieval in forest ecosystems and enhancing wildfire management efforts.

Terminologization of Colouronyms of Metal and Mineral Etymological Origin: The Evolutionary Aspect

This paper is devoted to the study of French colouronyms and their terminologization in different subtypes of scientific discourse. The research is based on colouronyms of mineral origin “(jaune / vert / brun / rouge d’) andalousite” and “quartz (blanc / rose),” as well as those of metallic origin – “jaune d’or” and “jaune / vert de chrome.” The authors carry out an etymological analysis of all the above-mentioned lexical units and consider the main tendencies of the terminologization of words with the semantics of colour in French scientific discourse. The peculiarities of the terminologization of colouronyms of mineral origin are analysed in the chemistry and geology discourse subtypes, whereas trends in the use of colouronyms of mineral origin are presented in the chemistry and physics discourse subtypes.The aim of this paper is to analyse the evolutionary tendencies of the terminologization of French colouronyms in the subtypes of scientific discourse to describe their common and diverging properties at different levels, i.e. structural-semantic, morphological, and syntactic.The empirical material for the research comprises written scientific texts on chemistry(the Annales de chimie et de physique electronic archive of scientific journals), geology (the Planète-Terre electronic archive of scientific journals), and physics (the Futura Sciences electronic archive of scientific journals).The research methods employed include etymological, component, structural-semantic, morphological, and syntactic analyses.The terminologization of colouronyms is a long-term continuous evolutionary discursive process that can be observed in French scientific discourse from the nineteenth century until the present day. The authors investigate the evolutionary tendencies of colouronyms as emerging semantics of colour in lexemes that have not had it before, the development of new structural-semantic models, the increasing numbers of morphological categories of colour terms, the acquisition of new syntactic roles, and the gradual process of the semantic transformation of colouronyms. The transition of colouronyms from one subtype of scientific discourse into another while preserving the structural-syntactic forms and morphological and semantic categories indicates the correlation between the discursive subtypes and the convergence of their functional properties at different levels of the language system. Despite this interdiscursive connectedness, each of the subtypes manifests distinct boundaries, which can be revealed through the study of the semantic specificity of colouronyms and their terminologization in scientific discourse.Transl.: Volkova M.V. 2022. Terminologization of Colouronyms of Metal and Mineral Etymological Origin: The Evolutionary Aspect. Professional Discourse & Communication. 4(2). P. 45–65. DOI: https://doi.org/10.24833/2687-0126-2022-4-2-45-65

“Cansada, agobiada, frustrada”: Desafíos emocionales de docentes de inglés en primaria durante la pandemia: Estudio de caso en San Carlos, Costa Rica

Introduction. COVID-19’s health crisis put a hold on all educational processes worldwide. This was not the exception for Costa Rica at the Ministry of Education. In March 2020, it was proposed that distance education was going to be put into practice. Purpose. This paper focuses on the feelings the participants communicated during the study. This was part of a principal investigation that used a case study methodology based on inductive qualitative research. It had the purpose of describing the perceptions of four English primary school teachers from the Dirección Regional de Educación San Carlos (San Carlos Regional Education Office) regarding distance education during the sanitary emergency of COVID-19. Methodology. Data were collected through in-depth interviews, lesson plans, and video recordings of pedagogical mediation and professional contexts. The data were analyzed using semantic categories from content analysis (Hatch, 2002) and the Web Qualitative Data Analysis (webQDA) software (Costa et al., 2019). Results. Results suggest that teachers reported experiencing a range of emotions before and during the pandemic, with the majority being negative in nature. Among the most notable were uncertainty, stress, confusion, overwork, a sense of difficulty, sadness, and worry. Other feelings included discrimination, impotence, and disrespect towards the teachers and the families’ specific contexts. This study presents the teachers from their human dimension as professionals who endured the pandemic within their own educational contexts. The study also shows the importance of effective communication and taking into consideration psychological support to strengthen the emotional integrity of the teachers.