Common Words Research Articles

A Research on Conjunctions and Interjections Jointly Used in Turkish and Urdu

Common words and grammatical similarities between languages, increase the interaction of languages with each other and accelerate the language learning process. Over the centuries, many grammatical subjects from Arabic and Persian have been transferred to Turkish and Urdu under the influence of the religion of Islam. Urdu, which comes from ordo/ordu in Old Turkic, means khan’s mansion, military camp. Nowadays, Urdu language appears as a common means of communication of different cultures in many parts of the Indian Subcontinent. Scientific studies in the literature show that the common words used today between Turkish and Urdu languages are mostly of Arabic and Persian origin. This study put emphasis on the common conjunctions that connect the sentences and concepts related to the meaning used in Turkish and Urdu languages and common exclamations that describe emotional states or reflect a nature sound; it was tried to draw attention to sound changes and meaning changes with the examples of sentences given in the form of tables.

Depressed and Ostracized: Scrutinizing the Condition of Dalit Women in India and Their Fight for Justice Against Barbarism

Dalit women bear the heaviest burden of discrimination since they are the focus of the repressive social tiers. This is an attempt to highlight the situation of these marginalized groups using many factors that sociologists frequently use to assess the status of a class. Although depressed women’s literacy rates have grown from 41.9% in 2001 to 56.5% in 2011, there is still a long way to go before they reach the general category’s literacy rate of 64.6%. As a result, Dalit women continue to live in appalling circumstances. Regardless of the unfair terms and circumstances of employment, they are forced to work in poverty that compels them to toil irrespective of the unjust terms and conditions of employment. Even among the depressed castes, the men get precedence over any job. Discrimination and oppression are the most common words that can be used to describe the position of Dalit women. Weak physique and unhealthy living condition are other issues which, contributes to their misery, making them vulnerable to the competitive world. The words ‘violence’, ‘suffering’ and ‘agony’ seem synonymous with these women. Atrocities against Dalit women are only emphasized by caste standards. Hence, much awaits to be done to guarantee justice based on gender and ensure equality in all instances. They are true ‘The Depressed and Ostracized’.

Studies conducted on physical activity and brain: a bibliometric analysis

Purpose: The purpose of the study is to examine physical activity and brain-related studies published in international and peer-reviewed journals using the Bibliometric Analysis Method. Material and Methods: By applying the visualized Bibliometric Analysis Method in the study, it was aimed to examine the general distribution characteristics, study progress, and highlights of existing studies on the effects of physical activity on the brain from 2001 to 2022. Studies that were published in journals containing SSCI, SCI-Expanded and AHCI indices of Web of Science (WoS) were examined within the scope of the study. After the elimination criteria were applied, a total of 6007 studies were included in the Bibliometric Analysis. Results: When the year distributions of the publications were examined, it was found that they were few in number from 1990 to 2000, increased as of 2001, and a significant increase was detected especially in 2019. According to the articles reviewed based on WoS citations, it was found that the fields of neuroscience, clinical neurology and psychiatry came to the forefront. Considering the journal distribution of the examined articles, the PLoS One journal was in the first place. Four main clusters emerged in the common word analysis, which were green, red, blue and yellow. It was obtained that the green cluster occupying the center of the map was the field of sports sciences. The words found in the clusters that emerged from the analysis were physical activity, exercise, cognition, aging, Alzheimer’s disease, depression,dementia, and hippocampus. Conclusion: The study systematically summarizes and analyzes studies conducted on the promotion of physical activity by emphasizing the improvement of cognitive functions. It is expected that the study will provide guidance and reference for future research.

36 Lexical Frequency and Semantic Fluency Performances in Cognitively Normal APOEε4 Carriers

Objective:There is a pressing need for sensitive, non-invasive indicators of cognitive impairment in those at risk for Alzheimer’s disease (AD). One group at an increased risk for AD is APOEε4 carriers. One study found that cognitively normal APOEε4 carriers are less likely to produce low frequency (i.e., less common) words on semantic fluency tasks relative to non-carriers, but this finding has not yet been replicated. This study aims to replicate these findings within the Wake Forest ADRC clinical core population, and examine whether these findings extend to additional semantic fluency tasks.Participants and Methods:This sample includes 221 APOEε4 non-carriers (165 females, 56 males; 190 White, 28 Black/African American, 3 Asian; Mage = 69.55) and 79 APOEε4 carriers (59 females, 20 males; 58 White, 20 Black/African American, 1 Asian; Mage = 65.52) who had been adjudicated as cognitively normal at baseline. Semantic fluency data for both the animal task and vegetable task was scored for total number of items as well as mean lexical frequency (attained via the SUBTLEXus database). Demographic variables and additional cognitive variables (MMSE, MoCA, AMNART scores) were also included from the participants’ baseline visit.Results:APOEε4 carriers and non-carriers did not differ on years of education, AMNART scores, or gender (ps > 0.05). APOEε4 carriers were slightly younger and included more Black/African American participants (ps < 0.05). Stepwise linear regression was used to determine the variance in total fluency score and mean lexical frequency accounted for by APOEε4 status after including relevant demographic variables (age, sex, race, years of education, and AMNART score). As expected, demographic variables accounted for significant variance in total fluency score (p < 0.0001). Age accounted for significant variance in total fluency score for both the animal task (ß = -0.32, p <0.0001) and the vegetable task (ß = -0.29, p < 0.0001), but interestingly, not the lexical frequency of words produced. After accounting for demographic variables, APOEε4 status did not account for additional variance in lexical frequency for either fluency task (ps > 0.05). Interestingly, APOEε4 status was a significant predictor of total words for the vegetable semantic fluency task only (ß = 0.13, p = 0.01), resulting in a model that accounted for more variance (R2 = 0.25, F(6, 292) = 16.11, p < 0.0001) in total words than demographic variables alone (R2 = 0.23, F(5, 293) = 17.75, p < 0.0001).Conclusions:Unsurprisingly, we found that age, AMNART, and education were significant predictors of total word fluency. One unexpected finding was that age did not predict the lexical frequency - that is - regardless of age, participants tended to retrieve words of the same lexical frequency, which stands in contrast to the notion that retrieval efficiency of infrequent words declines with age. With regard to APOEε4, we did not replicate existing work demonstrating differences in lexical frequency and semantic fluency tasks for ε4 carriers and non-carriers; possibly due to differences in the demographic characteristics of the sample.

67 Are these familiar words? Analyzing the utility of a new Spanish verbal memory test for children in North Texas

Objective:The American Academy of Clinical Neuropsychology’s (AACN) Relevance 2050 Initiative goals highlight the need for new assessment methods that are inclusive of the rising heterogeneous population in the US. In 2022, the Texas Education Agency reported that approximately 20% of the student population in Texas public schools are English learners (ELs), and approximately 90% of them are Spanish-speaking. In an effort to address the need for more Spanish cognitive measures normed in the US, a pediatric neuropsychology research team in North Dallas developed the Spanish Verbal Memory Test for Children (SVMT-C). To assist with establishing its validity, this study aims to corroborate that the words chosen for the list are familiar to children of different cultural backgrounds in North Texas.Participants and Methods:Enrollment of healthy, Spanish-speaking children between 6.0 and 17.11 years old within the Dallas Fort-Worth (DFW) metroplex began in January 2022 and continues to date. Study participation entails completing an in-person testing session with the child, while the parent/legal guardian completes a word-related survey along with other forms. The parent survey asks parents to indicate their child’s knowledge of 45 words (15 are target words). The testing session with the child includes completion of several cognitive tests (e.g., SVMT-C, EOWPVT-4:SBE) and a posttest survey that measures the child’s knowledge of the target words on the SVMT-C. The EOWPVT-4:SBE was used to estimate vocabulary level in Spanish to support proficiency determinations.Results:To date, 23 parent-child dyads have participated in the study, and 7 different countries of origin are represented in the overall sample. Data of children who earned SS<85 in Spanish on the vocabulary test were omitted, leaving the pediatric sample at n=20. Ages ranged between 6.2 and 15.2 years old. Eighteen children were bilingual, one was monolingual, and one was multilingual. Only Spanish-speaking parents completed the Spanish Verbal Memory Test Survey, leaving the parent sample at n=21. The child survey revealed that 95% of the children (19 of 20) knew all 15 target words; only one 6-year-old child did not know a word, which contradicted their parent’s report. The parent survey revealed that 90% of parents reported their child knew all 15 target words and 100% of parents reported their child knew 14 of 15 words; only two parents (19 of 21) were unsure if their child knew one of the words but the child then earned a score of 100% on their survey.Conclusions:Creating a verbal memory measure in Spanish for use in the US presents a set of unique challenges because of the variability in terminology that exists in the language. Lack of familiarity with terminology may influence performance and invalidate results. In this endeavor, the goal was to recognize these nuances and create a relevant measure that uses common words for Spanish-speaking children regardless of cultural background. Thus far, the data supports the appropriateness of the words listed in the SVMT-C with a 100% familiarity rate among children ages 7 to 15 years old.

Automatic construction of classification dimensions by clustering texts based on common words

A way to efficiently manage large sets of texts is to construct multiple dimensions on texts and manage the texts from the dimensions. The key problem is to use a suitable representation model of text to classify texts into class trees representing dimensions. Traditional vector-based distance definitions are unsuitable to restrict the semantic relations between texts and measure the similarity of a set of more than two texts. Frequently occurred common words within a set of texts represent the texts but traditional frequent-term-set based text clustering approaches and the topic model are unsuitable for generating class trees on texts. This paper proposes an approach to using common words to represent texts and measuring the similarity of a class of texts by calculating the sum of the weights of common words of the class that indicate the common semantics of these texts. The idea is in line with the characteristics of human classification of texts. A bottom-up text clustering approach is proposed to construct class trees of texts. The common words of each class on the class trees are used as the label of the class to indicate the common semantics of the class and manage the texts of the class. Therefore, the approach can be used to construct multiple classification trees on texts. The experiments for evaluating the classification accuracy and the structure of the constructed class trees show that our approach is better than other clustering algorithms. A document summarization approach based on our approach reaches a good performance.

The Example of Teaching False Equivalent Words in Teaching Turkish to Kyrgyz

Frequent use of common words in teaching Turkish to Kyrgyz helps to increase students’ attention to the lesson. However, some words may cause translation problems because their spelling and pronunciation are the same but their meanings are different. In this framework, it is important to take into consideration the false equivalents between dialects when teaching Turkish to Kyrgyz students. In this study, activities for teaching false equivalents are proposed. In line with this purpose, the research was designed with a qualitative approach and it was aimed to make students recognize false equivalents at A1 basic level by interacting in the classroom. When selecting false equivalents, the frequency of use in the target language was carefully considered and it was planned to teach the related words by role-playing them. As a result of the study, it is expected that students will recognize false equivalent words and show interest in dialogue activities. In addition, it is also thought that it will provide interaction among students. It is thought that the study will contribute to the teaching of false equivalent words.

Developing a Word Lexicon from Electronic Health Records for Natural Language Processing Analysis of Free-Text Reports for Patients with Venous Thromboembolism.

Patients with gliomas, particularly glioblastomas (GBM) have a higher risk of developing venous thromboembolism (VTE), including both pulmonary embolism (PE) or deep vein thrombosis (DVT), both correlating with overall survival (OS) and potential biological signaling relevance for analysis of the tumor state in conjunction with evolving biomarkers. Artificial intelligence (AI) approaches thar employ VTE as a clinical feature in brain tumor patients is understudied due to the difficulty in analyzing unstructured clinical data in electronic health records (EHR). Data expansion by creating a word lexicon for natural language processing (NLP) of free-text clinical reports will allow exposure of VTE for classification of large-scale data sets, NLP and AI. Patients with a pathologic diagnosis of GBM (2005-2021) were extracted from EHR and screened for the development of VTE based on radiology free-text reports - ultrasound (US) of extremities and Computed Topography-pulmonary angiogram (CT). Language data were collected manually and verified computationally using radiology report text filtration. Common phrases and words employed were collected to generate a lexicon for future computational analysis. Kaplan-Meier survival analyses were generated for VTE in relation OS and progression-free survival (PFS). A total of 163 patients (mean age = 56.1 ± 12.1, 65% male) were included, 48 (29.4%) were screened for VTE following clinical suspicion on history or physical exam, and 15 (9.2%) were found to have a VTE. Screening methods were US 83.3% (40) or CT 13.9% (6), or both 4.6% (2). 28.6% (12) of US and 37.5% (3) of CT resulted in a positive VTE diagnosis. Terms that were only detected in patients with VTE were for US: "complete" 44%, "thrombosis detected" (8.3%), "occlusive" (16%), "partial" (16%), "critical" (25%), "residual" (8.3%) and for CT: "critical" (67%) and "pulmonary emboli" (33%). Words used in free-text reports showed a high degree of inter-reporter variability. The words "partial", "residual", "complete", "critical" or "clotted" when used as an "or" Boolean statement applied to US and CT radiology reports identified ∼93% of the patients with VTE. Patients with VTE had worse OS (median 14 vs. 19 months, p = .0189) and PFS (median 6 vs. 9 months, p = .0239) compared to patients without VTE indicating underlying pathology associated with both prevalence of VTE and tumor burden and the need to incorporate VTE as a clinical feature in large scale data analyses. US and CT yield a similar percentage of positive VTE findings while employing different terms to characterize VTE. We confirm that patients with VTE have poorer outcomes and present a word combination that identifies patients with VTE in large-scale radiology report data. Further research will focus on validation in other datasets, NLP approaches to optimize and data aggregation with laboratory data and large-scale omics panels to expose thrombosis as a clinical feature for biomarker analysis.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BackgroundTargeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.ObjectiveThis scoping review...

The Problem of Vocabulary in Indonesian and Malaysian from Malaysia Students at UIN Sunan Ampel Surabaya

Students from Malaysia who study in Indonesia are mostly Malay speakers in their home countries, however they have trouble understanding Indonesian in common word forms. Especially when students produce scholarly papers, theses, or other academic assignments. This latest study investigates the Indonesian language comprehension problem among Malaysian students at UIN Sunan Ampel Surabaya. This research focuses on uncovering issues related to what problems they experience and the factors that cause these problems to arise. The qualitative descriptive method was applied in this study through observation and interviews. This study discovered that the key to Malaysian students' language proficiency is their comprehension of both spoken and written language. The factors that make them experience problems in understanding Indonesian are derived from internal and external factors (their adapting environment). External influences have a significant impact on Malaysian students' ability to learn Indonesian verbally and in writing.

Nöropsikoloji Alanında Yapılan Akademik Çalışmaların Son 20 Yılı Üzerine Bir Bibliyometrik Analiz

Bibliometric analysis is an analysis study that examines the developments and academic studies in a field to be examined with methods such as common word analysis, citation analysis, bibliometric matching. When bibliometric analysis is applied in a very comprehensive field such as neuropsychology, it provides a good road map for researchers to determine the areas they will work in. However, when the literature on psychology and its subfields is reviewed, it is seen that the number of bibliometric analysis studies is quite low. For this reason, this study aims to analyze the last 20 years of academic studies in the field of neuropsychology. For this purpose, in the first stage of the research, articles on "neorupsychology" were searched in the WoS database, and the fields of psychology, clinical psychology, psychiatry, neuroscience, experimental psychology, behavioral sciences, developmental psychology, pediatrics, educational psychology, neuroimaging, biological psychology, social psychology, applied psychology and psychoanalytic psychology, which were considered to be most related to the field of neuropsychology, were selected. Common word analysis and social network analysis were performed on the articles containing the related words, and concept maps were created to enable visualization. Accordingly, it was observed that words such as "schizophrenia", "psychosis", "depression" lost their influence, albeit slowly, while words such as "assessment", "performance validity", "clinical neuropsychology", "rehabilitaion" increased their influence. It can be said that current studies in the field of neuropsychology have shifted away from focusing on a specific clinical condition and towards studies that include neuropsychological assessments and rehabilitation approaches.

Text mining of veterinary forums for epidemiological surveillance supplementation

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.

Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records

BackgroundData mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency.MethodsIn this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, and rules supporting a variety of NLP downstream tasks can further be built with only minimal manual feature engineering and tagging required from clinical experts. Together, these steps create an open processing pipeline that gradually refines the data in a transparent way, which greatly improves the interpretable nature of our method. Data transformations are thus made transparent and predictions interpretable, which is imperative for healthcare. The combined method also has other advantages, like potentially being language independent, demanding few domain resources for maintenance, and able to cover misspellings, abbreviations, and acronyms. To test and evaluate the combined method, we have developed a clinical decision support system (CDSS) named Information System for Clinical Concept Searching (ICCS) that implements the method for clinical concept tagging, extraction, and classification.ResultsIn empirical studies the method shows high performance (recall 92.6%, precision 88.8%, F-measure 90.7%), and has demonstrated its value to clinical practice. Here we employ a real-life EHR-derived dataset to evaluate the method’s performance on the task of classification (i.e., detecting patient allergies) against a range of common supervised learning algorithms. The combined method achieves state-of-the-art performance compared to the alternative methods we evaluate. We also perform a qualitative analysis of common word embedding methods on the task of word similarity to examine their potential for supporting automatic feature engineering for clinical NLP tasks.ConclusionsBased on the promising results, we suggest more research should be aimed at exploiting the inherent synergies between unsupervised, supervised, and rule-based paradigms for clinical NLP.

A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy

Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the category of web pages based on the text content of the web pages. Due to the unavailability of a matrix representation that can handle long web page text content, this study employs a document representation known as word cloud image to visualise the words extracted from the text content web page after data pre-processing. The most popular words are shown in large size and appear in the centre of the word cloud image. The most common words are the words that appear frequently in the text content web page and are related to describing what the web page content is about. The Convolutional Neural Network (CNN) recognises the pattern of words presented in the core portions of the word cloud image to categorise the category to which the web page belongs. The proposed model for web page classification has been compared with the other web page classification models. It shows the good result that achieved an accuracy of 85.6%. It can be used as a tool that helps to make identifying the category of web pages more accurate

Is there a ‘universal’ core? Using semantic primes to select vocabulary across languages in AAC

Core vocabulary lists and vocabulary inventories vary according to language. Lists from one language cannot and should not be assumed to be translatable, as words represent language-specific concepts and grammar. In this manuscript, we (a) present the results of a vocabulary overlap analysis between different published core vocabulary lists in English, Korean, Spanish, and Sepedi; (b) discuss the concept of universal semantic primes as a set of universal concepts that are posited to be language-independent; and (c) provide a list of common words shared across all four languages as exemplars of their semantic primes. The resulting common core words and their corresponding semantic primes can assist families and professionals in thinking about the initial steps in the development of AAC systems for their bilingual/multilingual clients.

Police Brutality

Police brutality is a common word we hear around us. It is an offence which is a violation of civil rights. To describe this term, it means police are using excessive force to apprehend another person. Excessive force might include physical and verbal harassment of an individual. There is no only one misconduct or negative act done by police but there are other misconduct forms as well which includes false arrest, beating, sexual assault, racial abuse, torture, indiscrimination, unlawful killing, mental injury and property damage.

An Analysis on the Cognitive Structure and Opinions of Pre-Service Science Teachers on the Concept of Micro Teaching

In this study, it is aimed to examine the cognitive structures of pre-service science teachers regarding the concept of “micro teaching”. The study was carried out with 25 teacher candidates studying in the 4th grade of science teaching at Alanya Alaaddin Keykubat University Faculty of Education in a 4-week period within the scope of the teacher practice course in the fall semester of the 2021-2022 academic year. In the study, since it was aimed to reveal the cognitive structures of pre-service science teachers regarding the concept of “micro teaching”, screening method was used. In the collection of the study data, “independent word association test, drawing-writing and concept map” were used. Content analysis was performed in the analysis of the data obtained from the study. When the findings obtained as a result of the study were examined, the categories obtained from three different measurement tools had features that support and explain each other. “Part of micro teaching, teaching practice, self-assessment and tools used in micro teaching” obtained from all three measurement tools emerged as common and dominant categories. The most commonly used answer words by pre-service teachers for the category of “part of micro teaching” were determined as “pre-service teacher, planning, presentation, group work, lesson, student, teaching, information, education, registration, teaching, self-evaluation, class, re-teach and re-plan”. The most commonly used answer words for the category of “teaching practice” by pre-service teachers were determined as “skill, development, experience, technique, experimentation, performance, ability, observation, reducing anxiety and practice”. The most commonly used answer words by pre-service teachers for the category of “self-evaluation” were determined as “evaluation, review, feedback, monitoring and feedback”. The most common answer words used by teacher candidates for the category of “tools used in micro teaching” were determined as “video, camera and tape”. When the drawings made by the pre-service teachers for approximately micro teaching are examined according to the levels, the drawings of 50% of them are partial drawings and conceptual representative drawings. They explained the concept of micro teaching in ways related to scientific facts. This situation can be evaluated as the cognitive structures of pre-service science teachers related to the concept of micro teaching are sufficient.

Lost in Translation: Overcoming Distinctions in Worldviews in Environmental Impact Assessments in Canada and Russia

How would the usage of Indigenous languages contribute to overcoming the epistemological gap between Traditional Ecological Knowledge and Environmental Impact Assessments? This article examines incommensurabilities that arise in Sakha-Russian and Cree-English translations of EIA through the translations of the most common words in samples. Without being embedded in Indigenous languages, TEK and other knowledges are easily decontextualized, and results in the loss of layers of meaning. This study adopted a linguistic anthropological approach to language combined with content analysis and guided by a poststructuralist mode of analysis. We argue policies around EIA/EAs must be shifted to center Indigenous languages as the source of TEK and ensure that there is space for these languages to be used in the consultation processes.

Object labeling and disambiguation in 4-month-old infants.

The account that word learning starts in earnest during the second year of life, when infants have mastered the disambiguation skills, has recently been challenged by evidence that infants during the first year already know many common words. The preliminary ability to rapidly map and disambiguate linguistic labels was tested in Italian-speaking infants (N = 96, 47 boys; age = 4 and 6 months, eye tracking). Infants can rapidly map linguistic labels to objects and movements, and disambiguate the intended referents to novel words, but they fail with sinewave analogs. In hearing infants, mapping and disambiguation emerge early in development, and are flexible as to which visual referents infants are willing to map to linguistic labels, but may be constrained to linguistic sounds.

The Teacher Image Interpretation in Student Teachers: A Linguistic Anthropology Approach

The study aims to determine whether the images of a teacher in the normative document and the minds of student teachers coincide, as well as to identify the differences. A total of 236 students of the Faculty of Philology and History participated in the associative experiment. The words “ability”, “readiness”, and “possession” are the most common words used in the normative documents of the Russian Federation in the context of higher education. The concept of “teacher” is the main stimulus word in the associative experiment. The results of the associative experiment revealed a group of the most frequent associative words. In addition, based on the quantitative indicators of the associative experiment, nine thematic groups of associative words were formed. A comparative analysis of the indicators of an associative experiment involving students from different countries can make it possible to compare the features of national education systems and show their effect on the formation of a teacher image in the minds of student teachers.