Words Research Articles

Incidental vocabulary recognition effects of subtitled, captioned and reverse subtitled audiovisual input

This study compares effects of brief exposure to L1 subtitled, L2 captioned, and reverse subtitled audiovisual input on three aspects of vocabulary learning: meaning, form, and pronunciation of target language words. A within-subjects design was used, in which three video clips in the different viewing modes were shown to ten L1 Spanish participants who underwent a pre-test, post-test, and delayed post-test of English terms that each occurred in just one of the videos. The tests measured recognition of meaning, form, and pronunciation through translation into the L1, dictation, and reading the target words aloud with native speaker ratings, respectively. The findings show statistically significant gains in producing the accurate written form of vocabulary in reverse subtitled and L2 captioned video clips, and mixed results for the other variables, including statistically significant gains in pronunciation of vocabulary with L1 subtitles.

THE IMPACTS OF DIEGETIC STRATEGIES ON THE LOCALIZATION QUALITY OF GENSHIN IMPACTS' ACHIEVEMENT FEATURES

The aim of this thesis is to find out how the strategy of achievement on Genshin Impact term is translated from English into Indonesian in video game. This research uses qualitative method to explore the objectives of the research. The researcher collected data through several steps which aims are: to find the translation strategy contained in the video game, to classify and analyze the types of translation strategy from Achievements on Genshin Impact based on Purnomo translation strategy in video games theory, then determine the most dominant type of translation strategy and describe the translation strategy of Genshin Impact and finally concluded the result. The results of this research showed that Purnomo's (2019) proposed strategies for translating video games are used, 149 data were found, classified into the following categories: 2 Abbreviation, 13 Condensation, 13 Omission, 8 Symbolization, 88 Retention, and 25 Mediation. Therefore, the dominant data is Retention, which means that Retention is used to preserve source language words or terms that are considered and accepted conventionally by gamers and become fatal if translated. Based on the analysis, the localization quality assessment reveals that Achievements in Genshin Impact localization achieve a "Good" rating, with an average score of 2.8.

A Benchmark for Morphological Segmentation in Uyghur and Kazakh

Morphological segmentation and stemming are foundational tasks in natural language processing. They have become effective ways to alleviate data sparsity in agglutinative languages because of the nature of agglutinative language word formation. Uyghur and Kazakh, as typical agglutinative languages, have made significant progress in morphological segmentation and stemming in recent years. However, the evaluation metrics used in previous work are character-level based, which may not comprehensively reflect the performance of models in morphological segmentation or stemming. Moreover, existing methods avoid manual feature extraction, but the model’s ability to learn features is inadequate in complex scenarios, and the correlation between different features has not been considered. Consequently, these models lack representation in complex contexts, affecting their effective generalization in practical scenarios. To address these issues, this paper redefines the morphological-level evaluation metrics: F1-score and accuracy (ACC) for morphological segmentation and stemming tasks. In addition, two models are proposed for morpheme segmentation and stem extraction tasks: supervised model and unsupervised model. The supervised model learns character and contextual features simultaneously, then feature embeddings are input into a Transformer encoder to study the correlation between character and context embeddings. The last layer of the model uses a CRF or softmax layer to determine morphological boundaries. In unsupervised learning, an encoder–decoder structure introduces n-gram correlation assumptions and masked attention mechanisms, enhancing the correlation between characters within n-grams and reducing the impact of characters outside n-grams on boundaries. Finally, comprehensive comparative analyses of the performance of different models are conducted from various points of view. Experimental results demonstrate that: (1) The proposed evaluation method effectively reflects the differences in morphological segmentation and stemming for Uyghur and Kazakh; (2) Learning different features and their correlation can enhance the model’s generalization ability in complex contexts. The proposed models achieve state-of-the-art performance on Uyghur and Kazakh datasets.

Subword representations successfully decode brain responses to morphologically complex written words

Abstract This study extends the idea of decoding word-evoked brain activations using a corpus-semantic vector space to multimorphemic words in the agglutinative Finnish language. The corpus-semantic models are trained on word segments, and decoding is carried out with word vectors that are composed of these segments. We tested several alternative vector-space models using different segmentations: no segmentation (whole word), linguistic morphemes, statistical morphemes, random segmentation, and character-level 1-, 2- and 3-grams, and paired them with recorded MEG responses to multimorphemic words in a visual word recognition task. For all variants, the decoding accuracy exceeded the standard word-label permutation-based significance thresholds at 350--500 ms after stimulus onset. However, the critical segment-label permutation test revealed that only those segmentations that were morphologically aware reached significance in the brain decoding task. The results suggest that both whole-word forms and morphemes are represented in the brain and show that neural decoding using corpus-semantic word representations derived from compositional subword segments is applicable also for multimorphemic word forms. This is especially relevant for languages with complex morphology, because a large proportion of word forms are rare and it can be difficult to find statistically reliable surface representations for them in any large corpus.

Types of Chinese loanwords and their ways of translation into Russian

Foreign loanwords are one of the sources of replenishment of the vocabulary of any language. Words of foreign languages make up a significant proportion of Chinese neologisms. The purpose of this study is to classify borrowings in modern Chinese and briefly discuss the methods of their translation into Russian. The object of this article is loanwords in Chinese neologisms based on dictionary materials, and the subject of this work is the types of Chinese loanwords. The material selected for this article is based on the dictionary of "Chinese-Russian dictionary of new words and expressions". This article briefly analyzes the classification of Chinese loanwords, split into three categories: phonetic, semantic and phonetic-semantic loanwords. Then summarizes their main methods of translation using examples. The following methods were used in the work: random selection, descriptive, contextual and transformational analysis. The component analysis method was used to analyze individual words in the text. In conclusion, the types of loanwords from the dictionary since the XXI century are analyzed and the results obtained are summarized. It is shown that when translating Chinese borrowings into Russian, transcription, transliteration, lexico-semantic substitutions, and explication of these four translation methods can be mainly used. The scientific novelty of this article lies in the fact that, based on the analysis of specific examples, it systematizes three types of Chinese loanwords and discusses letter loanwords unique to the Chinese language, which enriches the current results of the study of Chinese loanwords. On the other hand, with the development of time and the constant appearance of new words, the need to translate new Chinese vocabulary into other languages has become acute. This article attempts to provide some reference value to the Russian translation of Chinese loanwords.

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.

Language Translator Using Deep Learning

Abstract: Digital translation (MT) is a process of translating text from one language to another using software by including both computer and language information. Initially, the MT system acquires text translation into the source language by simply associating the meaning of the words in the source language with the target language with the help of grammar. However, such methods did not produce good results due to their failure to capture the various sentence structures in the language. This process of translation is time-consuming and requires skilled craftsmen in both languages. Subsequently, integrated translation methods such as mathematical translation (SMT) and neural translation (NMT) technology have been introduced to address the challenges of legal-based approaches. MT has already shown promising results in bilingual translation. In contrast to SMT, which requires sub-components trained separately in translation, NMT uses one large neural network for training. This paper outlines how to train a repetitive neural network for rearrangement for a source to identify. The default encoder helps to reconstruct the vectors of the target language. Therefore, powerful hardware (GPU) support is required. The GPU improves system performance by reducing training time.

아동양육시설 초등학생들의 기초문해력 실태 조사

The purpose of this study was to examine the level of basic literacy skills among elementary school students in residential child care institution based on their reading development. A total of 118 students participated in the reading screening test. According to the results of the screening test, students with severe reading difficulties were classified as ‘at risk’, students with reading difficulties were classified as ‘some risk’, and students with average and above average reading levels were classified as ‘typical’. The ‘at risk’ students were administered the Korean language acquisition and word recognition tests, while students in the ‘some risk’, and ‘typical’, were administered the reading fluency and comprehension tests. The main research findings are as follows. First, elementary school students in residential child care institution generally showed low levels of basic literacy skills. Second, almost students in the ‘at risk’ group were found to have difficulty in word recognition. Third, students in the ‘some risk’ and ‘typical’ groups were found to have greater difficulty in reading comprehension than reading fluency. Based on these research results, implications for enhancing basic literacy skills of elementary school students in residential child care institution were proposed.

Figurative Language of Song Lyrics in Akimoto Yasushi’s Works Sung by Keyakizaka46

The language used in song lyrics generally has an aesthetic, poetic style of language that contains meaning to entertain the audience. Song lyrics are a place for an author to express feelings about his experiences or the other people's lives. In a song lyric, the words are selected and adjusted so that the meaning and message of the song lyrics reaches the audience directly or indirectly. Indirectly conveying song content usually uses figurative language or figurative words such as conceptual metaphors. This study aims to describe the meaning and types of conceptual metaphors found in 3 song lyrics by Akimoto Yasushi performed by the idol group Keyakizaka46. Most of the songs created by Akimoto performed by Keyakizaka46 have the theme of differences and disputes, they performed the song without showing a smile, this is one of the uniqueness of Keyakizaka46. The method used is descriptive qualitative method. The theory used is Lakoff & Johnson's conceptual metaphor theory. The results of the study found 13 metaphors, consisting of 6 types of structural metaphors, 1 type of orientational metaphor, and 6 types of ontological metaphors. The metaphors in these 3 song lyrics by Akimoto Yasushi have meaning, namely an invitation for poets to dare to speak out and have an opinion in choosing, not to be afraid of disputes and differences, and to invite them to live life according to their own will and not be controlled by others. The use of metaphors in Akimoto's song lyrics shows the uniqueness and characteristics of the Keyakizaka46 idol group.

Experimental study on short-text clustering using transformer-based semantic similarity measure

Sentence clustering plays a central role in various text-processing activities and has received extensive attention for measuring semantic similarity between compared sentences. However, relatively little focus has been placed on evaluating clustering performance using available similarity measures that adopt low-dimensional continuous representations. Such representations are crucial in domains like sentence clustering, where traditional word co-occurrence representations often achieve poor results when clustering semantically similar sentences that share no common words. This article presents a new implementation that incorporates a sentence similarity measure based on the notion of embedding representation for evaluating the performance of three types of text clustering methods: partitional clustering, hierarchical clustering, and fuzzy clustering, on standard textual datasets. This measure derives its semantic information from pre-training models designed to simulate human knowledge about words in natural language. The article also compares the performance of the used similarity measure by training it on two state-of-the-art pre-training models to investigate which yields better results. We argue that the superior performance of the selected clustering methods stems from their more effective use of the semantic information offered by this embedding-based similarity measure. Furthermore, we use hierarchical clustering, the best-performing method, for a text summarization task and report the results. The implementation in this article demonstrates that incorporating the sentence embedding measure leads to significantly improved performance in both text clustering and text summarization tasks.

Lexical-semantic activation in French-Spanish or French-English bilingual toddlers: An event-related potential (ERP) investigation

Both behavioral and neurophysiological evidence shows that lexical-semantic organization emerges by two years in monolingual children. Research in bilingual children is more scarce, and there is only a limited amount of neurophysiological evidence of the effect of language dominance on lexical-semantic activation. In the present event-related potential (ERP) study, we investigated whether bilingual French-Spanish and French-English learning 24- to 30-month-olds activate semantic relations between words similarly in their both languages, and whether the priming effects are similar in children learning two different language pairs. Participants were presented with related and unrelated dominant and non-dominant language word pairs in a within-language lexical-semantic priming paradigm. The amplitudes of N400 were modulated by trial type, language dominance and language group. A language-independent priming effect - more pronounced N400 amplitudes for unrelated than for related target words - was found in the group of toddlers learning French and Spanish. In the group of toddlers learning French and English, a priming effect was observed only in their non-dominant language. Our results propose that the language pair may contribute to lexical-semantic facilitation in priming tasks during early childhood.

Horizontal Chemistry.

In 1976 Raymond Williams commented, 'Culture is one of the two or three most complicated words in the English language.' Such implied difficulty has not prevented Bloomsbury Academic, since the 2000s, from publishing around forty series of their well-produced and generously illustrated Cultural Histories, with, according to their website, a further fifty in progress. Each series contains six volumes, each book covering, in theory, the same chronological period (antiquity, the Middle Ages, the Renaissance, the Enlightenment, the age of empire and the modern age), though there is some variation depending on precise topic. The idea is that one can use these books not only to read 'horizontally' about a subject across time, but also 'vertically' through different subjects in the same period-a idea made easier by the e-texts of the series on Bloomsbury's website.

Nature of common words in Kazakh and Turkish languages

Nature of common words in Kazakh and Turkish languages

Shared Decision-Making Training in Family Medicine Residency: A Scoping Review

Shared decisions, in which physicians and patients share their agendas and make clinical decisions together, are optimal for patient-centered care. Shared decision-making (SDM) training in family medicine residency is always provided, but the best training approach for improving clinical practice is unclear. This review aims to identify the scope of the literature on SDM training in family medicine residency to better understand the opportunities for training in this area. Four databases (Embase, MEDLINE, Scopus, and Web of Science) were searched from their inception to November 2022. The search was limited to English language and text words for the following four components: (1) family medicine, (2) residency, (3) SDM, and (4) training. Of the 522 unique articles, six studies were included for data extraction and synthesis. Four studies referenced three training programs that included SDM and disease- or condition-specific issues. These programs showed positive effects on family medicine residents’ knowledge, skills, and willingness to engage in SDM. Two studies outlined the requirements for SDM training in postgraduate medical education at the national level, and detailed the educational needs of family medicine residents. Purposeful SDM training during family medicine residency improves residents’ knowledge, skills, and willingness to engage in SDM. Future studies should explore the effects of SDM training on clinical practice and patient care.

Natural language sentiment as an indicator of depression and anxiety symptoms: a longitudinal mixed methods study1

ABSTRACT The study tested how the use of positive- (e.g. beautiful) and negative-valenced (e.g. horrible) words in natural language and its change in time affects the severity of depression and anxiety symptoms among depressed and non-depressed individuals. This longitudinal mixed methods study (N = 40 participants, n = 1440 narratives) with three measurements within a year showed that at the between-person level the use of negative-valenced words was strongly associated with the increase in anxiety and depression symptoms over time while the use of positive-valenced words was slightly associated with the decrease in anxiety and depression symptom. These effects were not supported for within-person level (i.e. changes in word usage). No significant differences were observed in the effects between depressed and non-depressed groups. Summing up, the overall use of positive- and negative-valenced words (particularly negative-valenced words) had a stronger effect on the severity of psychopathological symptoms than their change over time. The results were discussed in the context of natural language processing and its application in diagnosing depression and anxiety symptoms.

AI Powered Sign Language Translator

The purpose of this effort is to promote equitable environments and allow people with hearing disabilities to communicate in their native language by proposing an AI-based sign language translator. We used a transformer neural network, which can analyze over 500 data points from a person's face and gestures, to translate sign language into text. The translator can expand, produce new datasets, and build models for sign language recognition thanks to our machine learning process. As a proof of concept, we developed an interpreter for emergency calls using more than 200 sign language words. The main goal is to empower people who are deaf to participate in social, political, economic, and cultural spheres of life. We see a lot of people with illnesses, including blindness, deafness, and dumbness, every day. They have trouble interacting with other people. The suggested method can translate sign language into text and voice since this study describes two-way communication between deaf, dumb, and normal individuals. Key Words: Sign Language, Inclusion, Social Development, Artificial Intelligence, Machine Learning.

The poetry of psychological distance: Bidirectional associations between stimulus speed and its psychological distance and construal level.

Based on the cognitive-ecological approach and on logical-functional principles, in 12 studies (11 preregistered), we examine the novel hypotheses that psychological distance and construal level (CL) are associated in people's minds with stimulus speed: the psychologically distant/abstract is slow, and the psychologically close/concrete is fast. The findings support our expectations. Study Set I examined the association between psychological distance and speed. Findings show that psychological distance is implicitly and explicitly associated with speed (Study 1), that psychological distance is seen as compatible with slow and proximity with fast (Study 2), that stimulus psychological distance affects its perceived speed (Study 3), and that stimulus speed affects its psychological distance (Study 4). Study Set II examined the association between construal level and speed. Findings show that construal level is explicitly associated with speed (Study 5), that abstract is seen as compatible with slow and concrete with fast (Study 6), that natural language word distribution structures reflect an association between abstractness and speed (Study 7), that construal level affects speed (Study 8), and that speed affects stimulus construal level (Study 9). Study Set III examined implications for communication and person perception. Findings suggest that slow-paced (vs. fast-paced) speech is associated with larger perceived spatial and social distance between speaker and audience and larger audiences (Studies 10a, 10b) and that people infer an expansive (contractive) regulatory scope from slow-paced (fast-paced) spoken messages (Study 11). We elaborate on possible mechanisms and their theoretical and practical implications in domains including decision making and urban design. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Обучение хорватских учащихся лексике русского языка с опорой на родной язык

This article discusses the main reasons underlying the difficulties and errors experienced by Croatian native speakers in mastering the lexical structure of another Slavic language, specifically Russian, in the initial stage of learning. The author's attention is focused on the student’s substitution of meanings of lexical units in Russian with words of the native language, due to interlingual or linguistic paronymy. In teaching Russian to Croatian native speakers, it is important to maintain the thematic and situational principle of teaching, the principle of sufficiency, and the principle of relying on the mother tongue. This paper provides examples of effective vocabulary mastering: the use of visual clarity, reliance on similar sounding words in the native language, internationalisms, archaisms, application of the tacit (implicit) knowledge mechanism, unconscious reliance on equivalent, similar sounding words from close South Slavic languages, particularly Serbian and Slovenian (as positive interference). Attention is focused on the development of logical thinking and independence, the correct use of the dictionary, and working with audio recordings and video materials. The article focuses on words with connotative meaning, linguocultural lexis, non-equivalent and partially equivalent lexis, and speech etiquette.

Disaster Resilient Infrastructure: A Bibliometric Analysis of Global Research Trends

The vital role of infrastructure in the delivery of essential services and the severity of disasters globally calls for more research and discourse on disaster resilient infrastructure. Despite the increasing body of knowledge on disaster resilient infrastructure, an assessment of research on the subject is lacking. This study fills this gap by assessing the global research trend in disaster resilient infrastructure. The study adopted a bibliometric analysis with the use of the VOSviewer. The main search words used in the study were “Disaster”, “Resilient”, AND “Infrastructure. Initial extracted papers with these keywords were 1041. After refining based on language, document type, and key words, a total of 278 papers were found suitable for the analysis. The years 2020 to 2022 indicated the highest research outputs while countries with highest number of publication on the subject of disaster-resilient infrastructure were the United States, United Kingdom and Japan. Most of the highly cited works were review-based, case studies and a few experimental. The findings of this study serve as a source of baseline data on disaster resilient infrastructure which can provide new research directions and also guide the design and implementation of policies on disaster-resilient infrastructure.

Intelligent speech elderly rehabilitation learning assistance system based on deep learning and sensor networks

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.