Semantic Word Research Articles

The language of marketing hyperbole and consumer perception-The case of Glasgow.

The aim of the study was to explore the potential of a timed word association task to generate detailed insights into the perception of Glasgow city and its people which could inform destination and brand marketing. Destination marketers have a challenge to convey the tourist destination image to attract and satisfy the expectations of its visitors. Yet destination perceptions are often the result of multiple tourist visitor experiences at a location, neglecting the voice of the resident. The extent to which word associations varied by participants' relationship to Glasgow was identified in terms of Aaker's brand personality scale, an extension of personality research on brands and destinations. Surveying of 1,219 UK participants generated a total of 5,993 terms (city; 1,144 unique) and 5,034 terms (people; 944 unique). The value of capturing the perceptions of a destination by its residents is identified. The results showed that the city of Glasgow was often described as cold and busy, while the people were primarily described as friendly and funny. Evidence was found in support of dual-processing theory suggesting word associations based on lived experiences of a city may be generated later (in terms of the order in which the terms were generated) in a word association task, while common linguistic associations (e.g. synonyms, antonyms, hierarchies etc.) tend to be generated earlier in the task. As hypothesised, analyses revealed a significant relationship between several of the Aaker-dimensions of brand personality, and the consumers' relationship to Glasgow, extending marketing research with an empirical approach to identifying differences in the perceived personality of a destination. The study offers a practical, fast, and replicable method for destination marketers to study consumer perception at scale, which is currently not widely utilised in this field. In particular, the use of semantic distance and word embeddings provides a readily available approach to automatically categorise content derived from word associations studies, or indeed, any text-based content. In contrast, financial investment in non-validated branding and destination marketing campaigns appear to be increasingly problematic. Advances were made in testing an approach to interpreting word associations through the lens of linguistic and situated simulation (LASS) theory to provide deeper analysis to both categorise and interpret consumers' perception. Traditional approaches to tourism marketing and destination branding rarely provide such a level of analytical appraisal. The analysis presented in this paper challenges the orthodoxy and validity of investment in brand and destination marketing at a city level and the potential for word association tasks to be used as a valuable alternative method to create more effective destination marketing and branding.

USING COGNITIVE LINGUISTICS TO TEACH SYNONYMS FOR IRAQI COLLEGE STUDENTS

It's challenging to help EFL students in Iraq learn and use synonyms. This study investigates the challenges faced by Iraqi English as a Foreign Language (EFL) students in understanding and employing synonyms. By employing Langacker's cognitive linguistic theory of domains, the research aims to enhance EFL Iraqi students' synonym knowledge and vocabulary acquisition. The study utilizes a descriptive analysis technique, with a pre-test and post-test design, involving fifty first-year students at the University of Baghdad's College of Languages, Department of English. The findings reveal that the domain theory significantly improved students' comprehension and application of English word semantics. Prior to the intervention, students had limited knowledge of the domain approach and struggled with recognizing and producing lexical features. After five weeks of treatment, students demonstrated increased abilities to define different types of domains and their properties, as well as understanding the literal and conceptual meanings of words. The study concludes that implementing the theory of domains can effectively enhance vocabulary acquisition among Iraqi EFL students and emphasizes the importance of teaching synonyms with a focus on sense relations.

Cross-modal Semantically Augmented Network for Image-text Matching

Image-text matching plays an important role in solving the problem of cross-modal information processing. Since there are nonnegligible semantic differences between heterogenous pairwise data, a crucial challenge is how to learn a unified representation. Existing methods mainly rely on the alignment between regional image features and corresponding entity words. However, the regional features in the image are often more concerned with the foreground entity information, and the attribute information of the entities and the relational information are ignored. How to effectively integrate entity-attribute alignment and relationship alignment has not been fully studied. Therefore, we propose a Cross-Modal Semantically Augmented Network for Image-Text Matching (CMSAN), which combines the relationships between entities in the image with the semantics of relational words in the text. CMSAN (1) proposes an adaptive word-type prediction model that classifies the words into four types, i.e., entity word, attribute word, relation word, and unnecessary word. It can align different image features at multiple levels. CMSAN (2) designs a sophisticated relationship alignment module and an entity-attribute alignment module that maximizes the exploitation of the semantic information, which enables the model to have more discriminative power and further improves the matching accuracy.

Discourse-based verbal working memory training and transfer effects for individuals with an amnestic type of mild cognitive impairment

PurposeThe purpose of the study was to investigate the treatment efficacy of a discourse-based working memory (WM) protocol for individuals with the amnestic type of mild cognitive impairment (MCI).MethodThe current study employed a randomised, single-blind design. Fourteen individuals with MCI participated in the study (n = 7 treatment group and n = 7 control group). The treatment protocol consisted of 10 sessions two times per week, and treatment was individually administered only to the treatment group. A Wilcoxon signed-rank test was performed to verify pre-post comparisons within each group. Mann-Whitney nonparametric tests were conducted to confirm the differences between the treatment and control groups for the post-treatment scores.ResultThe treatment group demonstrated a significant increase in story-retelling outcomes for both the treated stories and untreated novel stories compared to the control group. Furthermore, the treatment group presented transfer effects for WM span measures and controlled word association tasks.ConclusionThe results indicated that a discourse-based WM treatment protocol is efficacious for the amnestic type of mild cognitive impairment with the effects transferred to frontal lobe functions, as measured by WM tasks and semantic word fluency measures. Further studies are needed to track the trajectory of performance across sessions.

Al-Tahlil al-Dalālī Lilafẓi al-Khairi fī al-Qur’ān al-Karīm ‘inda Wahbah al-Zuhailī

The purpose of this research is to find out the semantics meaning and interpretation of the word al-khair according to Wahbah az-Zuhaili. This study uses a type of library research, where the discussion and interpretation of the verses of the Qur'an are studied from books and library documentation which has an object according to the study's title. The research method used is descriptive to collect comprehensive data on the data needed by the author, then compile it using primary and secondary data. The method of analysis is by analyzing and drawing conclusions and the values ​​obtained after studying the verses of al-khair according to Wahbah az-Zuhaili. The result of the analysis semantics of al-khair words in terms of the Qur’an says that its action in the world is all good deeds between the servant and His Lord. At times, the term 'al-khair' in the Quran means all the worldly benefits that people desire.Then, it is followed by the meaning of reward, indicating the rewards from Allah. Other times, the term 'al-khair' comes with the meaning of one of the blessings in this world, and blessings refer to the rewards in the Hereafter.

Analysis of the Unbalanced Phenomenon of the Antonyms "More" and "Less"

"More" and "Less" are a pair of antonyms that we often use in both spoken and written language. They always appear in pairs, but there are significant differences in the semantic scope, word formation, collocation, and frequency of use between the two, resulting in an imbalance of antonyms. This article mainly analyzes the imbalance phenomenon of this pair of antonyms from the four aspects mentioned above.

A sentiment analysis method for COVID-19 network comments integrated with semantic concept

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy.

“There was much new to grok”: an analysis of word coinage in science fiction literature

Abstract As can be witnessed in projects such as The Oxford Dictionary of Science Fiction (Prucher, Jeff. 2007. Brave new words: The Oxford dictionary of science fiction. Oxford: Oxford University Press), science fiction has been fertile ground for the creation of new words and concepts. Whereas the aforementioned dictionary was constructed by eliciting examples and citations from volunteers, this paper presents an initial foray into data-driven methods for uncovering lexis unique to science fiction. Words unique to science fiction texts are extracted by comparing a science fiction corpus against the British National Corpus (BNC Consortium. 2007. The British National Corpus, XML edition. Oxford Text Archive. http://hdl.handle.net/20.500.12024/2554 (accessed 29 June 2022)) to produce a list of 306 neologisms from 74 texts. In addition, this study seeks to examine the ways in which authors impart the meaning of such words to the reader, drawing on frameworks of semantic word relations and work in cognitive linguistics. This reveals the use of definitions and glosses by the authors, both in narration and direct speech, co-occurrence with synonyms, and the drip-feeding of attributes pertaining to the concept being referenced. In addition, characters can be shown to struggle with the concepts to which neologisms refer, allowing authors to explore themes of alienation and other-worldliness.

Exploring the Role of Animal Phrases in English and Russian

Phrases are used to express emotions and states of mind. Phrases are a special lexical level of any language, reflecting the culture of the people and are widely used in various fields of communication. The aim of this paper is to study animal phrases in English and Russian. Firstly, phraseology is studied as a science, phrases are categorized and the concept of " animal " is defined. The second part of the discussion is devoted to the semantic classification of English and Russian phrases with the word "animal" and their comparative analysis. The semantic character of the phrases depends on the semantics of the animal words, which reflect the everyday experience of Russians and Englishmen, their historical and cultural experience, their national mentality and the uniqueness of their evaluation of the world around them. The study of English and Russian phrases allows us to gain a deeper insight into the history and character of the two peoples, to compare the pictures and imagery of the two countries, and thus to better understand their languages. Both languages play a fundamental role in our time. Phrases have already been the subject of a great deal of linguistic research.

Unified Fake News Detection System (UFNDS) Framework

The deliberate spread of misleading or inaccurate material pose as authentic news is known as "fake news." Its increasing prevalence calls for the creation of practical strategies to recognize and counteract its negative effects on people and society. Previous methods of identifying fake news depended on linguistic signals and stylistic components. However, these methods faced limitations in terms of their applicability and accuracy. To overcome these constraints, this study proposes the utilization of an extended stacking ensemble classification algorithm (ES-ECA), a machine learning technique designed specifically for detecting fake news. By employing this innovative approach, we aim to surpass the existing barriers and enhance our ability to combat misinformation. The ensemble classifier outperformed the individual classifiers, with an accuracy of 75.18% and an F1-score of 81.81%. These findings imply that the suggested algorithm can be utilized to lessen the negative effects of fake news on society and is efficient at identifying it. The EHT-DL model leverages a multi-step approach to effectively detect fake news. It begins with preprocessing steps such as text normalization, special character handling, stemming, stop word removal, tokenization, and lemmatization. This ensures the dataset is clean and ready for subsequent processing. Feature extraction is performed using TF-IDF, N-grams, and word embeddings scores to capture semantic information and word importance. After that, the dataset is divided into training and testing sets and the deep learning model Dl4jMlpClassifier is used to classify the data. To tackle the drawbacks of existing techniques, the EHT-DL model incorporates efficient hyperparameter tuning. It uses both Grid Search and Random Search methods to optimize the Dl4jMlpClassifier's hyperparameters. By using this method, the model's accuracy and capacity to distinguish between authentic and fraudulent news are both improved. The effectiveness of the EHT-DL model is shown by the experimental findings. Standard assessment criteria including accuracy, precision, recall, and F1-score are used to assess the model. In terms of accuracy and efficiency, comparisons with current methods demonstrate the superiority of the proposed model in identifying bogus news (83.27% accuracy, 80.62% precision, 71.57% recall, and 75.63% f1-score). To increase classification accuracy and resilience, OE-MDL combines the phases of optimized deep learning (ODL) and optimized machine learning (OML). An optimized Multilayer Perceptron serves as the Meta classifier in the OML phase, on top of base classifiers such as optimized RandomForest, optimized J48, optimized SMO, optimized NaiveBayes, and optimized IBk. The experimental findings show that the OE-MDL algorithm performs better than other methods with the maximum recall (85.18%), accuracy (84.27%), precision (74.17%), and F1-Score (79.29%), providing a practical means of halting the spread of false information. The framework for the Unified Fake News Detection System (UFNDS) is revealed. The final result of the research works and demonstrates how the three main stages—Deep Learning and Optimized Ensemble Machine (OE-MDL), Efficient Hyperparameter-Tuned Deep Learning Model (EHT-DL), and Enhanced Stacking Ensemble Classification Algorithm (ES-ECA)—are cohesively incorporated into the UFNDS framework. We examine the UFNDS framework's architectural design and show how flexible it is to the always changing problems associated with fake news identification.

Трансграничная динамика англоязычного военного дискурса в период специальной военной операции РФ на Украине

The aim of the research is to characterise the transboundary dynamics of the English-language military discourse during Russia’s special military operation in Ukraine. The research is carried out involving the material of the publications of leading English-language news agencies devoted to the conduct of the special military operation of the Russian Federation in Ukraine. It is noted that the armed conflict, being a phenomenon covering all spheres of society, affects the language, which is not only the main means of communication for representatives of an ethnic group, but also a weapon that influences and manipulates public consciousness. The scientific novelty of the research lies in the fact that it is the first to analyse the impact of the armed conflict taking place today on the lexical structure of the English language. The paper identifies the main trends of changes in the lexical structure of the language during the transition of units of one discourse into the space of mass media discourse. It is the first time that modern mass media are considered as a kind of cross-discursive space in which lexical and phraseological units of the military discourse acquire new shades of meaning, allowing them to act as a weapon of influencing the consciousness of the mass audience. As a result of the research, it has been found that military terminology is being activated in mass media discourse, the semantics of words and phrases are changing, the vocabulary of World War II and the Cold War is being actualised, in addition, lexical and phraseological units of military slang are being introduced into media discourse.

Investigation on author’s concept in the Russian National Corpus

The data analysis of the Russian National Corpus (RNC) is being carried out by an increasing number of researchers, since the choice of lexical units and their description are now radically facilitated by corpus linguistics, which compiled all available texts in a particular database language based on a computer analysis and presented on the open Internet. The purpose of the study is to identify and describe the conceptual cognitive features and structural modeling of the CIVIC CONSCIOUSNESS concept in the works of Ye.A. Yevtushenko, i.e.: (1) based on the RNC data, to determine the place of the CIVIC CONSCIOUSNESS concept in the Russian social and artistic worldviews; (2) based on Yevtushenko’s works, to identify the features of the artistic CIVIC CONSCIOUSNESS concept in the individual author’s worldview; and (3) to characterize the interaction of the national and individual author’s worldviews in Yevtushenko’s works. To this end, the research involved general scientific methods of observation, description, and generalization as well as the method of conceptual analysis. The analysis of the RNC material shows that the structure of the CIVIC CONSCIOUSNESS concept is represented by five classes of features: (1) etymological and denotative motivating signs of the word representative of the concept; (2) conceptual attributes objectified as semantic word components; (3) figurative features; (4) categorical features; and (5) values (examples were taken from the main corpus of the Russian language as well as from the newspaper and poetic corpora). The analysis of the CIVIC CONSCIOUSNESS concept in Yevtushenko’s works suggests the anthropocentricity of its figurative component. Its content is revealed in terms of the Russian national worldview and, at the same time, the individual author’s worldview of Ye.A. Yevtushenko. As the analysis of ways of conceptualizing the CIVIC CONSCIOUSNESS concept has shown, the most actual characteristics in Yevtushenko’s worldview are conceptual figurative features, including: anthropomorphic, animate and inanimate ones. In addition, categorical (dimensional, qualitative, etc.) and value attributes of the CIVIC CONSCIOUSNESS concept were identified. The analysis of Yevtushenko’s literary texts made it possible to identify some occasional signs of the CIVIC CONSCIOUSNESS concept, in particular, a figurative sign of inanimate nature and a temporal attribute related to categorical features.

METHODS OF TRANSFERRING FULL WORD-FORMATION AFFIXES IN THE NATIONAL CORPUS OF THE KAZAKH LANGUAGE

This scientific article studies methods of transferring full word-formation affixes in the National Corpus of the Kazakh language. Affixes play an essential role in the forming of word formation and semantics of a language, and the study of their transmission is crucial for linguistic theory and practice. The purpose of the study is to analyze and systematize approaches to word-forming affixes in the corpus of the Kazakh language.The main areas of research include the analysis of the morphological features of affix transfer, as well as the identification of contexts in which various transmission options for transfer are possible.A short description of the scientific and practical significance of the work: the study allows us to better understand the proc esses of word formation in the Kazakh language and develop effective methods for automatic processing of texts in this language. It is also of practical importance for the creation of linguistic resources and the development of language applications. Brief description of the research methodology: the research methodology includes the analysis of textual material from the national corpus of the Kazakh language, as well as the use of linguistic tools and data analysis to determine the features of the transmission of affixes. The main results and analysis of the research work, conclusions: the study revealed various ways of transmitting wordforming affixes, including morphological constructions, in the corpus of the Kazakh language. The analysis made it possible to identify the features and frequency of these methods. Value of the conducted research: this study contributes to the linguistic theory, and enriches our knowledge about the mechanisms of word formation and ways of transmitting linguistic elements in the language corpus. In addition, the results obtained can be useful for the development of language technologies and resources for the Kazakh language. Practical significance of the results: the study can be used in the creation of automatic systems of text processing systems in the Kazakh language, and the development of language applications, such as in linguistic training and translation. The obtained results and conclusions can become the basis for further research in the field of linguistics and computational linguistics.

Отражение особенностей семантической структуры древних названий цвета в эволюции семантики общеславянских цветообозначений *bronъ и *polvъ

The aim of the research is to identify the peculiarities characterising the evolution of the semantics of the Common Slavic colour terms *bronъ and *polvъ, which reflect the specific features of the semantic structure of ancient colour names. The scientific novelty of the study lies in describing the specificity of the semantic structure of ancient colour terms through the history of not the main colour names, but rather the colour names that are “peripheral” from a modern perspective, namely, броный (derived from the Common Slavic *bronъ) and половый (derived from the Common Slavic *polvъ). The semantic features of the Common Slavic colour terms *bronъ and *polvъ, as well as their Indo-European proto-forms and Old Russian derivatives were identified through component analysis. As a result, it has been found that the evolution of the semantics of these Common Slavic colour terms is characterised by the absence of the initial semantic connection with the reference object, the presence of a semantic connection with light or its absence and the syncretism of the denoted colour tone. Thus, it has been revealed that the Common Slavic lexemes *bronъ and *polvъ were more likely to be names for colouration rather than colour itself. These characteristics reflect the specific features demonstrating differences in the perception of colour by ancient and modern linguistic consciousness that are common for the semantics of words developing colour meanings in the Russian language.

VOCABULARY OF THE MIDDLE BUG AREA DIALECTS IN THE DIALECT DICTIONARY

The article presents a fragment of the “Dictionary of the dialects of the Middle Bug area”. The object of the study is agricultural vocabulary documented in the dialects of the Middle Bug area. The subject of the study is the organization of this lexical layer in the dialect dictionary. The sources of the factual material are audio recordings of vernacular speech with a total duration of 205 hours 54 min. 13 s., made in 74 settlements of the Middle Bug area during the period of 2014–2016. The article uses general scientific methods of research: analysis, synthesis and observation, which made it possible to identify and systematize the dialect material; the descriptive method was used in the analysis of vernacular speech. Special linguistic research methods: questionnaires and interviews contributed to the documenting of dialect speech, contextual and interpretive analysis allowed to identify transformations of the semantic scope of lexemes functioning in the speech of speakers of the MiddleBug dialects, linguogeographical method made it possible to illustrate the areology of the documented names. The article describes the structure of a dictionary entry and presents a fragment of the “Dictionary of the dialects of the Middle Bug area”. The register lexemes are marked with letter abbreviations of the names of the examined settlements and with the references to the “Etymological Dictionary of the Ukrainian Language” (1982–2012), the “Eleven-volume Dictionary of the Ukrainian Language” (1970–1980), the “Dictionary of the Ukrainian Language edited by B. D. Hrinchenko” (1907), and the “Dictionary of Ukrainian Dialects of Odesa Region” edited by O. I. Bondar (2011) if the respective word is documented as a dialectism in the above lexicographic sources. In order to reveal the semantics of a register word and describe in more detail the specifics of the Middle Bug area dialects, sentences and microtexts of dialect speech with the obligatory indication of the settlement where they were recorded have been added. The peculiarities of pronunciation are reflected and illustrative material is presented using the general phonetic transcription for recording Ukrainian literary and dialect speech, which is proposed in the first volume of the “Atlas of the Ukrainian Language”. The analyzed lexical material emphasizes the originality of the Middle Bug area dialects, as evidenced by pronounced lexical, phonetic and, less often, accentual and grammatical variants of dialect names.

Research on the mechanism of teaching culture in Civics course based on deep learning model

Abstract This paper uses convolutional neural networks to construct a deep learning-based teaching mechanism for question-and-answer on civic culture. The one-hot representation and the distributed representation of words in the distributed expression form of natural language are analyzed. Word2Vec, a word embedding training tool, is used to generate word embedding representations, and CNN is applied to store and classify word semantics so as to generate semantic matrices. The attention mechanism is combined to simplify the model processing steps and reduce the complexity of operations. The deep learning hierarchical semantic extraction model is tested by loss function and relative similarity to judge the accuracy and performance of the model’s operation. The results showed that the mean composite score of the experimental group was 5.2 points higher than that of the control group, and the t-test results P=0.003<0.05 indicated that there was a significant difference between the overall assessment scores of the two groups, and the teaching of Civic Culture Quiz was beneficial to the improvement of students’ performance.

Hearing loss, depression, and cognition in younger and older adult CI candidates.

Hearing loss in old age is associated with cognitive decline and with depression. Our study aimed to investigate the relationship between hearing loss, cognitive decline, and secondary depressive symptoms in a sample of younger and older cochlear implant candidates with profound to severe hearing loss. This study is part of a larger cohort study designated to provide information on baseline data before CI. Sixty-one cochlear implant candidates with hearing loss from adulthood onwards (>18 years) were enrolled in this study. All had symmetrical sensorineural hearing loss in both ears (four-frequency hearing threshold difference of no more than 20 dB, PTA). Individuals with primary affective disorders, psychosis, below-average intelligence, poor German language skills, visual impairment, and a medical diagnosis with potential impact on cognition (e.g., neurodegenerative diseases,) were excluded. Four-frequency hearing thresholds (dB, PTA, better ear) were collected. Using the Abbreviated Profile of Hearing Aid Benefit, we assessed subjective hearing in noise. Clinical and subclinical depressive symptoms were assessed with the Beck Depression Inventory (BDI II). Cognitive status was assessed with a neurocognitive test battery. Our findings revealed a significant negative association between subjective hearing in noise (APHAB subscale "Background Noise") and BDII. However, we did not observe any link between hearing thresholds, depression, and cognition. Additionally, no differences emerged between younger (25-54 years) and older subjects (55-75 years). Unexpectedly, further unplanned analyses unveiled correlations between subjective hearing in quiet environments (APHAB) and cognitive performance [phonemic fluency (Regensburg Word Fluency), cognitive flexibility (TMTB), and nonverbal episodic memory (Nonverbal Learning Test), as well as subjective hearing of aversive/loud sounds (APHAB)], cognitive performance [semantic word fluency (RWT), and inhibition (Go/Nogo) and depression]. Duration of hearing loss and speech recognition at quiet (Freiburg Monosyllables) were not related to depression and cognitive performance. Impact of hearing loss on mood and cognition appears to be independent, suggesting a relationship with distinct aspects of hearing loss. These results underscore the importance of considering not only conventional audiometric measures like hearing thresholds but also variables related to hearing abilities during verbal communication in everyday life, both in quiet and noisy settings.

Semi-supervised semantic segmentation using cross-consistency training for pavement crack detection

Crack detection is crucial to providing information for assessing pavement condition and maintaining the safety of infrastructure. Traditional manual crack detection methods are relatively time-consuming and thus have gradually been replaced by automatic detection in recent years. However, most semantic segmentation algorithms require the manual labelling of a large amount of data, which consumes a great deal of time. A semi-supervised semantic word segmentation method based on cross-consistency training for road crack detection is established in this paper. To take advantage of unlabelled crack samples, this study enforced consistency between the primary and secondary decoder predictions, using different disturbed versions of the encoder output as inputs to improve the encoder representation. When only 60% of the annotated data were used, the applied method achieved a better performance than other mainstream semantic segmentation algorithms. Therefore, the applied method can be used in automated and efficient pavement detection for pavement inspection and maintenance.

Lexico-thematic groups of non-equivalent vocabulary in Russian translations (against the background of Chinese prototypes)

This article is centered on the study and description of the ways of identification and designation of the surrounding reality phenomena in two cultures - Russian and Chinese. Special attention is paid to the significance of the national-cultural specificity of the word. The article considers both general issues of ethno-cultural phenomena nomination and its specific cases, namely, the functioning of non-equivalent vocabulary and ways of its translation from Chinese into Russian. It also gives a brief overview of the distribution dynamics in modern Chinese prose and the problems of studying Yu Hua’s work in Russia. The aim of the article is to reveal the importance of translations of modern Chinese fiction as sources of studying non-equivalent vocabulary and translation strategies when working on characteristic word-markers of Chinese culture used in the novel “To Live” by the outstanding modern Chinese writer Yu Hua. The study is based on the translation of the novel in question, made by the Russian scholar R. G. Shapiro. The article determines thematic groups of non-equivalent vocabulary in the novel “To Live” (word-designations of new Chinese life, ethnographisms, historicisms, background and connotative words) and the main translation methods of their transfer (borrowing, description, hyperonyms, etc.). Special attention is paid to approximate or inappropriate translations in the text, due to the translator’s ignorance of the semantics of non-equivalent words, which ultimately leads to an imperfect understanding of Chinese culture.

Managerial sentiment and corporate risk-taking: evidence from Chinese listed companies’ annual reports

PurposeThis paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.Design/methodology/approachBy leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.FindingsResults show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.Research limitations/implicationsAnnual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.Practical implicationsTo the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.Originality/valueThis paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.