Similar Words Research Articles

Cognitive processes in EFL learners' reading comprehension: A comparative analysis of WhatsApp and traditional group-driven reading

WhatsApp (WA) has become increasingly popular in second language (L2) instructional practices, primarily for assessing L2 learners’ outcomes. Despite this popularity, there remains a significant gap in the literature regarding well-documented inquiries into the cognitive processes and reading comprehension of L2 learners. This study aimed to explain L2 learners’ reading comprehension abilities, and explore their cognitive processes between L2 learners who were taught using WA reading groups and those taught using the traditional group-driven reading approach. A mixed quantitative using an experimental design with 32 learners (12 males and 20 females) and a qualitative design was applied to address the two research purposes. The study results illustrated that learners' cognitive process in the WA reading group was effective, and their reading comprehension scores were higher than the traditional reading group. The effective cognitive strategies were visualized from learners’ WA chat histories. The WA reading group's cognitive process involved content discussion, self-reflection, and interpretation during online reading activities. They did not discuss from word to passage, but they tended to discuss the text's content and logical flow without finding the same difficult word for all group members. Conversely, the traditional reading group cognitive processes were less effective since the group member roles were not identified, such as all learners starting to find similar unfamiliar words and then discussing them again before interpreting the content of the texts. Study limitations and pedagogical implications from the study are adequately discussed.

Can large language models help augment English psycholinguistic datasets?

Research on language and cognition relies extensively on psycholinguistic datasets or “norms”. These datasets contain judgments of lexical properties like concreteness and age of acquisition, and can be used to norm experimental stimuli, discover empirical relationships in the lexicon, and stress-test computational models. However, collecting human judgments at scale is both time-consuming and expensive. This issue of scale is compounded for multi-dimensional norms and those incorporating context. The current work asks whether large language models (LLMs) can be leveraged to augment the creation of large, psycholinguistic datasets in English. I use GPT-4 to collect multiple kinds of semantic judgments (e.g., word similarity, contextualized sensorimotor associations, iconicity) for English words and compare these judgments against the human “gold standard”. For each dataset, I find that GPT-4’s judgments are positively correlated with human judgments, in some cases rivaling or even exceeding the average inter-annotator agreement displayed by humans. I then identify several ways in which LLM-generated norms differ from human-generated norms systematically. I also perform several “substitution analyses”, which demonstrate that replacing human-generated norms with LLM-generated norms in a statistical model does not change the sign of parameter estimates (though in select cases, there are significant changes to their magnitude). I conclude by discussing the considerations and limitations associated with LLM-generated norms in general, including concerns of data contamination, the choice of LLM, external validity, construct validity, and data quality. Additionally, all of GPT-4’s judgments (over 30,000 in total) are made available online for further analysis.

Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records.

COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.

Validation of a Thai artificial chatmate designed for cheering up the elderly during the COVID-19 pandemic

Background The COVID-19 pandemic severely affected populations of all age groups. The elderly are a high-risk group and are highly vulnerable to COVID-19. Assistive software chatbots can enhance the mental health status of the elderly by providing support and companionship. The objective of this study was to validate a Thai artificial chatmate for the elderly during the COVID-19 pandemic and floods. Methods Chatbot design includes the establishment of a dataset and emotional word vectors in which data consisting of emotional sentences were converted into the word vector form using a pre-trained word2vec model. A word vector was then input into a convolutional neural network (CNN) and trained until the model converges using sentence embedding and similarity word segmentation. Sentence vectors were generated by averaging each word vector using an averaged vector method. For approximate similarity matching, the Annoy library was used to create the indices in tree sorting. Data were collected from 22 elderly and assessed by the Post-Study System Usability Questionnaire (PSSUQ). Results The study revealed that 72.73% of the respondents found the chatbot easy to learn and use, 63.64% of the respondents found the chatbot can autonomously determine the next course of action, and 59.09% of the respondents believed that troubleshooting guidelines were provided for overcoming errors. The accuracy of the chatbot providing a reasonable response is 56.20±13.99%. Conclusions Most users were satisfied with the chatbot system. The proposed chatbot provided considerable essential insights into the development of assistance systems for the elderly during the coronavirus pandemic (COVID-19) and during the period of national disasters. The model can be expanded to other applications in the future.

On "False Friends" in the Field of Economics

“False friends” or bilingual homophones/homographs are words existing in two or more languages, sounding alike but differing significantly in meaning. They present a language learning challenge due to the inevitable confusion resulting from word migrations and similarities in sound and meaning. Coined in 1928, the term continues to fuel linguistic research, addressing comprehension confusions in the process of language acquisition. The reciprocal influences of languages, especially through neological borrowings, lead to divergent meanings in the target language. Inexperienced users may misuse these words due to homonymy. In economics, initially influenced by French borrowings, there is a substantial influx of terms from British or American English, driven by the need for precise terminology for new extralinguistic realities. This paper, drawing from teaching practice, provides brief examples and suggests strategies to navigate confusion related to translating „false friends,” emphasizing specific learning techniques and the use of online bilingual dictionaries.

Usefulness of the Simple Coma Scale, a Simplified Version of the Glasgow Coma Scale.

The Glasgow Coma Scale (GCS) is the most commonly used consciousness rating scale worldwide. Although it is a sensitive and accurate way of assessing a patient's level of consciousness, it is time-consuming and requires training. We designed the Simple Coma Scale (SCS) as a simplified version of the GCS. In this study, we examined whether the SCS could predict favorable neurogenic outcomes at discharge, survival, and GCS scores in patients with traumatic brain injury (TBI). We analyzed the data of 1,230 patients registered in the Japan Neurotrauma Data Bank (Project 2015) between April 2015 and March 2017. In the SCS, eye, verbal, and motor scores are given based on a 3-point scoring system, with similar wording ("Normal," "Something Wrong," and "None") used for all scores. The SCS is based on a 7-point scale. The Glasgow Outcome Scale was used to assess the outcomes. For the receiver operating characteristic (ROC) curves with the objective variable of good prognosis at discharge in the SCS and GCS, the area under the curve (AUC) for the SCS was 0.740 (95% confidence interval [CI]: 0.711-0.769), and that of the GCS was 0.757 (95% CI: 0.729-0.786). For ROC curves with survival as the objective variable, the AUC of the SCS was 0.751 (95% CI: 0.724-0.778), and that of the GCS was 0.764 (95% CI: 0.737-0.791). The SCS, similar to the GCS, may predict good prognosis and survival at discharge. Further analyses will continue to examine the usefulness and practicality of the SCS.

A Study of Innovative Entrepreneurial Behavior of College Students under Algorithmic Recommendation

Abstract In this paper, for a large amount of text content such as college students’ innovation and entrepreneurship education practice, considering the problem of directionality, based on the idea of a recommendation algorithm, we use word similarity and TextRank algorithm to extract the keywords of innovation and entrepreneurship behaviors as the first-knowledge labels of the textual resources, and introduce complex labeling network based on this. On the premise of semantic-based TextRank keywords and tag network, the fusion-gated graph attention group recommendation algorithm is used to construct college students’ innovation and entrepreneurship practice platform, and the simulation analysis of college students’ innovation and entrepreneurship practice platform is carried out. The results show that the survival rate of virtual startups can be calculated as 5.37% by using the college students’ innovation and entrepreneurship practice platform, which can accurately analyze that college students’ innovation and entrepreneurship behaviors are stronger, but the survival rate of the enterprises founded by college students for the first time needs to be improved. This study can provide theoretical knowledge guidance for the study of college students’ innovative entrepreneurial behavior and has a facilitating effect on the development of innovation and entrepreneurship education in colleges and universities.

A Study on C-E Translation Methods of Culture-Loaded Words: A Case Study of Selected Modern Chinese Essays

As a country competes for its overall national strength all over the world, culture has grown to be an increasingly significant aspect. Considering the differences in the development paths and cultural traditions of different countries, more sophisticated translation strategies and skills are needed in the process of cultural going out. From the cultural perspective, this paper takes Zhang Peiji’s English Translation of Selected Modern Chinese Essays as an example, selects and summarizes culture-loaded words full of Chinese characteristics, and analyzes their translation strategies and skills, including place names, salutations and four-character idioms, which are representative of culture-loaded words. This paper aims to provide referential strategies for the translation of similar culture-loaded words in other texts, and to solve the difficulties in the translation of culture-loaded words. After the case analysis, this paper found that the most commonly used translation methods of culture-loaded words should be literal and free translation, and that it is necessary to preserve the original meaning and reveal the accurate connotation of culture-loaded words in order to convey the uniqueness and characteristics of Chinese culture.

A Study of Computer-Assisted Automatic Translation Techniques and Practices for English Vocational Terminology

Abstract This paper mainly carries out a more in-depth study on the design and planning of computer-assisted translation models based on translation memory and demonstrates the new computer-assisted translation model formed in this way. The TMX specification model, combined with the actual needs, summarizes the corresponding terminology data model, introduces and summarizes the word splitting, similarity, and alignment techniques of the computer-assisted translation model, and verifies the effectiveness of the model through practical application. The study shows that the average code length of computer-aided translation is about 0.1~0.3 lower than that of human translation in specialized fields, and its performance and efficiency are better than that of human translation. The efficiency of computer-aided translation software in extracting terminology positively correlates with an increase in frequency. Computer-assisted translation plays a significant role in the professional field and provides empirical support for subsequent related research.

HIV serodifference: the "unsaid" in the social representations of health professionals.

To analyze social representations from the perspective of the silent zone of health professionals regarding HIV serodifference. This is a qualitative study, based on the theoretical-methodological framework of Social Representations, with the Theory of the Central Nucleus and Mute Zone by Jean Claude Abric. 51 professionals from specialized services from the metropolitan region of a capital in the northeast of Brazil participated, from October to December 2020. Interviews were applied using the free word association replacement technique, with prototypical and similarity analyzes processed in the Iramuteq software. The results showed that the central nucleus demonstrated the veiled presence of stigmas and lack of knowledge when dealing with HIV serodifference, represented by the expressions prejudice, lack of knowledge, fear, insanity, and love. The social representations attributed to the central core are anchored in prejudice, ignorance, fear, and insanity. Such meanings signal the need for improvements in the knowledge of health professionals involved in the health care network, in order to enable the praxis of care, and to confront the stigmas that permeate serodifference and cause distance between partners and health services.

Explicit expression of unconditionality using conjunctions in Chinese

This article discusses unconditionals in Chinese. Conjunctions with unconditional meaning, which serve for the explicit transmission of meaning in a sentence, are chosen as a subject of research. The purpose of the study is to find the conjunctions with explicit expression of unconditionality in the Chinese language. In addition to Chinese, the Russian language is also considered for a comparative analysis of conjunctions in different languages. A comparative analysis is carried out for all types of unconditionality after the description of each section. In this article not only conjunctions with unconditional meaning, but also correlates (words which clarify the meaning of the condition in the first part of the sentence) and the order of two clauses (main and dependent) are compared. The research methods include the search for conjunctions and correlatives from Chinese dictionaries and grammars for a further distribution of them in groups, consideration of conjunctions’ semantic, combination and stylistic features through dictionaries. As Chinese unconditional sentences have been little studied among Russian typologists, this article contains a certain scientific novelty. As a result, three types of unconditionals have been identified in total: absolute, irreal and real. Due to the differentiation of conjunctions in sentences with the absence of a condition, it was found out that a variety of similar words in the Chinese language is as rich as in the Russian one. At the end there are two tables with all the conjunctions both in Russian and in Chinese. In this article Russian is less emphasized because it has been researched earlier by other typologists. This material can be useful not only for sinologists, but also for the specialists in general linguistics.

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

Abstract Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.

Strategies for translating English filmonyms into Russian

The subject of the research in the article is the peculiarities of the translation of filmonyms from English into Russian. The methods of comparative, semantic and pragmatic analysis with elements of cultural and stylistic analysis are used in the work. The novelty of the research lies in the fact that it is based on the material of parallel English- and Russian-language filmonyms released over the past two decades. The obtained results contribute to the development of translation studies and the theory of onyms. Choosing a strategy is one of the most important decisions of the translator which is associated with the linguistic features of the filmonym and the cultural specifics of the translation audience. The closer the cultures of native speakers of the original language and the target language are, the more opportunities there are for using forenization, a translation strategy in which the translator strives to preserve the content and the form of the original title. Forenization strategy was used to transmit 25.34% of the English-language filmonyms of the sample, which is significantly inferior to the number of filmonyms translated according to the domestication strategy, which make up 69.34% of the sample. The high frequency of the domestication strategy is explained by the fact that using translation transformations allows overcoming such difficulties in translating filmonyms as discordance of interlanguage equivalents, mismatch of meanings of similar words, undesirable associations with precedent phenomena, dysphemisms, the emergence of meanings that disorient the viewer, untranslatable wordplay. In the examples that make up 5.32% of the sample, the binomial translation construction of the onym was used; the forenized title of the film is included in the first part before the colon, and the second part is a variant of the name adapted as part of the domestication strategy.

Validating Disaster Medicine Experts’ Feedback Outcome Using Artificial Intelligence Computing Techniques

Abstract Objectives This study aimed to use artificial intelligence (AI) computing techniques to determine if they can validate the findings of a previously published thematic analysis article focusing on disaster medicine experts’ open-ended feedback about Middle East and North African countries (MENA) for chemical, biological, radiological, and nuclear (CBRN) threats. Methods Automated text analytics techniques were employed to explore and visualize the semantic essence of the experts’ feedback through word vector transformation and Principal Component Analysis (PCA) for dimensionality reduction. The t-distributed Stochastic Neighbor Embedding (t-SNE) is another more advanced dimensionality reduction technique that enhanced the capturing of the determined components. Results Two prominent clusters emerged from the full textual data set representing word similarities groups in the original data set, denoting a thematic group of ideas that experts have emphasized in their responses. Upon deep reading the text feedback, the themes linked preparedness with different training types, such as tabletop exercises and policies/legislation. The findings are in line with currently adopted practices. Conclusions While AI methods demonstrated their valuable application in disaster medicine and helped validate the experts’ recommendations objectively, they should be approached cautiously, as they can be complex and challenging to comprehend fully.

Chinese and Vietnamese cross-lingual topic discovery based on word similarity of comparable corpus

Chinese and Vietnamese cross-lingual topic discovery based on word similarity of comparable corpus

Chinese and Vietnamese cross-lingual topic discovery based on word similarity of comparable corpus

Chinese and Vietnamese cross-lingual topic discovery based on word similarity of comparable corpus

Schema-congruency supports the formation of unitized representations: Evidence from event-related potentials

The main goal of the present study was to investigate whether schema-based encoding of novel word pairs (i.e., novel compound words) supports the formation of unitized representations and thus, associative familiarity-based recognition. We report two experiments that both comprise an incidental learning task, in which novel noun-noun compound words were presented in semantically congruent contexts, enabling schema-supported processing of both constituents, contrasted with a schema-neutral condition. In Experiment 1, the effects of schema congruency on memory performance were larger for associative memory performance than for item memory performance in a memory test in which intact, recombined, and new compound words had to be discriminated. This supports the view that schema congruency boosts associative memory by promoting unitization. When contrasting event-related potentials (ERPs) for hits with correct rejections or associative misses, an N400 attenuation effect (520–676 ms) indicating absolute familiarity was present in the congruent condition, but not in the neutral condition. In line with this, a direct comparison of ERPs on hits across conditions revealed more positive waveforms in the congruent than in the neutral condition. This suggests that absolute familiarity contributes to associative recognition memory when schema-supported processing is established. In Experiment 2, we tested whether schema congruency enables the formation of semantically overlapping representations. Therefore, we included semantically similar lure compound words in the test phase and compared false alarm rates to these lures across conditions. In line with our hypothesis, we found higher false alarm rates in the congruent as compared to the neutral condition. In conclusion, we provide converging evidence for the view that schema congruency enables the formation of unitized representations and supports familiarity-based memory retrieval.

Retracted: Accurate Estimation of English Word Similarity Based on Semantic Network

Retracted: Accurate Estimation of English Word Similarity Based on Semantic Network

Territory as a Public Law Category: Concept and Logical Relationship with the State

The article is devoted to several conceptual issues of legal understanding of the state territory. The author analyzes the construction of the notion of state territory and substantiates that its multiple definitions are theoretically acceptable. However, modern legal practice implies the inevitability of recognizing a specific approach to understanding state territory as a sphere where state sovereignty manifests itself. The author considers the discrepancies in the legally fixed descriptions of the RF territory composition. It is stated that the discussion about this composition has no scientific solution and can only be arbitrarily terminated, namely by means of enumerating the components of the territory in the legislation or recognizing the correctness of a certain approach by the academic community. The article addresses some issues of using the relevant terminology in legal doctrine and legal regulation. The author shows that legal and linguistic traditions of the describing of territorial issues in various countries do not agree, and that under certain conditions jurisprudence can technically manage without the term “territory” and similar words. Nevertheless, although the term “territory” is flawed, it has practically no alternative in modern Russian-language jurisprudence. The article reveals that in order to assess if a state requires territory, one should consider the interpretation of the notions of territory and state, as well the used scientific methodology. Besides, it demonstrates that it is methodologically precarious to interpret territory as a feature, element, condition or prerequisite of the state. The author shows that the spatial nature of the state bodies jurisdiction and of the national law operation, as well as the spatial structuring of such jurisdiction and of the legal order stay relevant for the modern period of the statehood development. However, the intensive digitalization implementation taking place in the sphere of public functions and public services can lead to a noticeable reduction of the practical need for territorial structuring of the state.

Language Policy in Turkic States and Societies Historical Aspect

The roots of the communication history of the Turkic language, which has a history of at least 1300 years as a written language, are, of course, much older. If we take into account the features of the words in the language of the Orhon-Yenisey monuments, which are the first written sources, and the similarities of words in other languages, we see that the Turkic language has a history of four to five millennia as a language of communication. The Turkic language, whose origin goes back thousands of years, has been influenced by many languages as a result of the cultural, social, and political relations of Turks with other nations. The settling of large territories by Turks, leading a sedentary and nomadic life, constantly migrating for new lands, and coming into contact with many peoples against the background of these events led to the contact of the Turkic language (the languages of the Turkic-speaking peoples and tribes) with many related and unrelated languages. As a result of these relations, sometimes the Turkic language influenced these languages from the lexical and even grammatical aspects, and sometimes, on the contrary, it borrowed words from those languages. In this research article, we will try to talk about the language policy in the Turkic state and societies and the developments in the historical process.