Features Of Words Research Articles

Hybrid architecture based intelligent diagnosis assistant for GP

As the first point of contact for patients, General Practitioners (GPs) play a crucial role in the National Health Service (NHS). An accurate primary diagnosis from the GP can alleviate the burden on specialists and reduce the time needed to re-confirm the patient’s condition, allowing for more efficient further examinations. However, GPs have broad but less specialized knowledge, which limits the accuracy of their diagnosis. Therefore, it is imperative to introduce an intelligent system to assist GPs in making decisions. This paper introduces two data augmentation methods, the Complaint Symptoms Integration Method and Symptom Dot Separating Method, to integrate essential information into the Integration dataset. Additionally, it proposes a hybrid architecture that fuses the features of words from different representation spaces. Experiments demonstrate that, compared to commonly used pre-trained attention-based models, our hybrid architecture delivers the best classification performance for four common neurological diseases on the enhanced Integration dataset. For example, the classification accuracy of the BERT+CNN hybrid architecture is 0.897, which is a 5.1% improvement over both BERT and CNN with 0.846. Finally, this paper develops an AI diagnosis assistant web application that leverages the superior performance of this architecture to help GPs complete primary diagnosis efficiently and accurately.

Severe processing capacity limits for sub-lexical features of letter strings

When reading, the visual system is confronted with many words simultaneously. How much of that information can a reader process at once? Previous studies demonstrated that low-level visual features of multiple words are processed in parallel, but lexical attributes are processed serially, for one word at a time. This implies that an internal bottleneck lies somewhere between early visual and lexical analysis. We used a dual-task behavioral paradigm to investigate whether this bottleneck lies at the stage of letter recognition or phonological decoding. On each trial, two letter strings were flashed briefly, one above and one below fixation, and then masked. In the letter identification experiment, participants indicated whether a vowel was present in a particular letter string. In the phonological decoding experiment, participants indicated whether the letter string was pronounceable. We compared accuracy in a focused attention condition, in which participants judged only one of the two strings, with accuracy in a divided attention condition, in which participants judged both strings independently. In both experiments, the cost of dividing attention was so large that it supported a serial model: participants were able to process only one letter string per trial. Furthermore, we found a stimulus processing trade-off that is characteristic of serial processing: When participants judged one string correctly, they were less likely to judge the other string correctly. Therefore, the bottleneck that constrains word recognition under these conditions arises at a sub-lexical level, perhaps due to a limit on the efficiency of letter recognition.

Enhancing fine-detail image synthesis from text descriptions by text aggregation and connection fusion module

Synthesizing images with fine details from text descriptions is a challenge. The existing single-stage generative adversarial networks (GANs) fuse sentence features into the image generation process through affine transformation, which alleviate the problems of missing details and large computation from stacked networks. However, existing single-stage networks ignore the word features in the text description, resulting in a lack of detail in the generated image. To address this issue, we proposed a text aggregation module (TAM) to fuse sentence features and word features in a text by a simple spatial attention mechanism. Then we built a text connection fusion (TCF) block consisting mainly of gated recurrent unit (GRU) and up-sampled block. It can connect text features used in the up-sampled blocks to improve text utilization. Besides, to further improve the semantic consistency between text and the generated images, we introduce the deep attentional multimodal similarity model (DAMSM) loss, which monitors the similarity between text and improves semantic consistency. Experimental results prove that our method is superior to the state-of-the-art models on the CUB and COCO datasets, regarding both image fidelity and semantic consistency with the text.

Analysis and Application of Civic Context Construction for University English Courses Combined with Cluster Analysis

Abstract This paper applies the cluster analysis method to classify different Civics contexts in university English courses, and improves the accuracy of Civics context text clustering by extracting the word stems of English Civics context texts. The text similarity calculation is used to remove feature words from the preprocessed text, to converge into a collection of feature words. The texts containing English Civic and Political Context are clustered and analyzed, and the clustering effect of Civic and Political Context texts is judged according to the clustering evaluation. The status quo of Civic and Political integration into English curriculum is explored from different dimensions, and corresponding optimization strategies are proposed with the converged data. The results show that the Civic-Political contexts of English courses are mainly divided into three categories, and based on the clustering of the Civic-Political contexts of university English courses, the contexts of “living, learning and doing” are relatively independent. The contexts “history, culture and society” and “science and technology” are closer. In integrating Civics and Politics into English teaching, the mean value of the dimension of teaching method is 4.212, which is in a better integration situation.

GCN-LSTM: multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks

Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the long31 term dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects.

Analysis of Hot Topics and Evolution of Research in World-class Agricultural Universities Based on BERTopic

Abstract In this paper, the cited papers and cited patents of world-class agriculture-related universities are taken as the research objects, and the preprocessing process of the research hotspot text of agriculture-related universities is accomplished by combining the participle processing method and the TF-IDF method. To make up for the shortcomings of the semantic level of the LDA model, BERTopic is utilized to obtain the document-topic distribution probability and the topic-topic word distribution probability, and the topic hotness of different topics is calculated. Then the Glove method is used to extract the word vector features of the topic words, normalize them, calculate the word vectors by taking the distribution probability values as the weighted weights, solve the topic vectors, and then calculate the similarity of the hot topics in different time windows by the cosine similarity to explore the hot topics and the evolution of the world’s first-class agriculture-related colleges and universities research.

Ancient cognate language relations and lexical-semantic features of borrowed words of western origin used in its vocabulary

Ancient cognate language relations and lexical-semantic features of borrowed words of western origin used in its vocabulary

Phraseological features of ancient Turkic words in Azerbaijani dialects

Phraseological features of ancient Turkic words in Azerbaijani dialects

Exploring English Translation Strategies Oriented by Big Data Technology

Abstract English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.

Phonology, homophony, and eyes-closed rest in Mandarin novel word learning: An eye-tracking study in adult native and non-native speakers

AbstractThis study used the visual world paradigm to investigate novel word learning in adults from different language backgrounds and the effects of phonology, homophony, and rest on the outcome. We created Mandarin novel words varied by types of phonological contrasts and homophone status. During the experiment, native (n = 34) and non-native speakers (English; n = 30) learned pairs of novel words and were tested twice with a 15-minute break in between, which was spent either resting or gaming. In the post-break test of novel word recognition, an interaction appeared between language backgrounds, phonology, and homophony: non-native speakers performed less accurately than native speakers only on non-homophones learned in pairs with tone contrasts. Eye movement data indicated that non-native speakers’ processing of tones may be more effortful than their processing of segments while learning homophones, as demonstrated by the time course. Interestingly, no significant effects of rest were observed across language groups; yet after gaming, native speakers achieved higher accuracy than non-native speakers. Overall, this study suggests that Mandarin novel word learning can be affected by participants’ language backgrounds and phonological and homophonous features of words. However, the role of short periods of rest in novel word learning requires further investigation.

Mining Text Value in Japanese Translation Teaching Based on KH Coder Text Data

Abstract Vocabulary serves as the cornerstone of Japanese translation instruction, with the identification of high-frequency terms within educational texts yielding significant pedagogical insights. This study employs the KH-Coder software to extract feature words frequently used in Japanese translation teaching materials. Through a combination of covariate network analysis, sentiment analysis, and hierarchical clustering, the paper delves into the intrinsic textual value of these educational resources. Furthermore, it integrates both generative and discriminative approaches to conduct dependency parsing of Japanese translation. By incorporating probabilistic functions, the methodology enhances contextual understanding, ensuring that each annotation unit within a sentence is optimally tagged. Analysis of the CGTT-35 corpus through KH-Coder reveals a list of the top 50 high-frequency words in Japanese translation teaching, each appearing over 100 times. This enables students to use these frequent words and sentence structures as cues to master common phrases and familiarize themselves with core content. The approach has proven effective in enhancing students’ receptivity to Japanese translation instruction and their comprehension capabilities, offering valuable implications for teaching strategies.

Research on the Digital Innovation and Inheritance Path of Southwest Lu Drum and Blow Music under the Background of Informatization

Abstract In this paper, we build a web crawler through the Scrapy framework to obtain the digitized data of Luxinan Drum and Blow music according to the demand, and construct the feature word vector and corpus of Luxinan Drum and Blow music by using the word2vec model. Using the three operations of pre-emphasis, frame-splitting and windowing, the digitized music of Lunan drums and dulcimers is digitized, and the Fourier transform of Lunan drums and dulcimers is used to obtain the cepstral coefficients of the Mel frequency, the fundamental frequency and the resonance peaks. The research object is selected, the questionnaire about the research of digital inheritance of the south Lu drum and blow music under the background of informationization is designed, and according to the initial data obtained for the research and analysis, the statistical analysis method is used to carry out the empirical analysis of the digital inheritance of the south Lu drum and blow music under the background of informationization. The results show that from the perspective of the inheritance value dimension of the Southwest Lu drum and blow music corpus, its mean and standard deviation are 2.11 and 0.958, respectively, indicating that many young people are increasingly concerned about the digital inheritance of the Southwest Lu drum and blow music. This study focuses on the analysis of the dilemma of inheritance and innovation in the new period and puts forward some feasible, innovative development paths to help this excellent national intangible cultural heritage to be inherited and developed.

Exploring the Direction of English Translation of Environmental Articles Based on Emotional Artificial Intelligence Learning Models

Abstract In this paper, we improve the performance of machine translation emotion recognition by fusing word features and linguistic features of environmentally friendly text with LSTM neural networks. Multi-channel LSTM’s unbalanced emotion classification method can address the problem of unbalanced numbers of samples in each emotion category in the emotion classification task. The bilingual semantic similarity degree feature is added to the translation process of English environmental protection data so as to make the translation of English environmental protection data more accurate. The proposed method is validated through experiments, and the results show that the translation quality of the LSTM model is generally higher than that of other models, and the mean value of the translation quality of the LSTM on the test17 dataset is 50.4. In terms of environmental protection terminology, mistranslations, common word mistranslations, etc. There is a significant reduction of 2–3 times in the absolute number of the LSTM model compared to the traditional method. A total of 94,788 corpora were obtained from 10 environmental translations, such as Silent Spring and Solace of the Heart, and 2,311 favorite emotion words were identified. Comparative analysis of the samples reveals that most of the translation results of some environmental terms are in the range of 0.7–0.9, and the range of semantic overlay between the machine translation results of the LSTM model and the standard translations is large. Most users believe the model improves the translator’s translation level and diction accuracy.

Analysis of Diversification of Intelligent Teaching in English Literacy Integrated Classroom Empowered by Artificial Intelligence Technology in Colleges and Universities

Abstract In recent years, an increasing number of English teachers have recognized the importance of reading and writing skills. Traditional English teaching methods often overlook the essential link between these skills, which hinders the improvement of students’ overall English proficiency. This paper introduces an adaptive Huber growth curve model to represent cognitive abilities in English reading. It develops an English reading ability detection model using a function-based approach. The model involves word feature extraction, fine-grained learning, and establishing information channels. These processes collectively contribute to a sequence annotation model that not only identifies but also automatically corrects errors in English writing samples. This facilitates integrated intelligent teaching of reading and writing skills. Analysis of English reading and writing capabilities among students at various colleges and universities reveals that the most notable improvement is in students’ critical thinking abilities during English reading, with most scores ranging between 3.5 and 4 and a total of 52 students participating. The difference in writing scores of the subject classes was 1.4186, of which the T value was 3.7855, the DF value was 112.3, and the P value was 0.000, which was significant, and the magnitude of the English writing scores of the subject class A was greater than that of the class B, which indicated that the integration of literacy and writing effectively improved the overall writing skills of the students.

ШЕШЕНДІК СӨЗДЕР ТАНЫМЫ

In this article, we will talk about the knowledge of public speaking. We are talking about the requirements, types and styles of public speaking, about artistic means and the quality of public speaking. The article analyzes and describes the role and meaning of the Kazakh oratorical words Tole bi, Kazybek bi, Aiteke bi, etc. It is concluded that the cognition and interpretation of the oratorical word are excluded from the laws of speech, the style of the oratorical word, the main features of the oratorical word.

Language representation of the concept of entropy – energy in the journalism of E. Zamyatin

The article is devoted to identifying the features of the linguistic representation of the idea of revolutionary renewal of the world, which is the most important for the worldview system of E. Zamyatin. The study was carried out in line with the anthropological trend that is relevant in modern linguistics. The focus is on vocabulary related to the reflection of the concept of entropy - energy transferred by E. Zamyatin from the field of physical and mathematical knowledge to the field of the humanities. Much attention is paid to the analysis of the functional features of words combined into a lexical-semantic group with a common meaning of the subjects of the revolutionary process, as well as the representation of the concept of revolution in the writer’s journalism. The main objectives of the study are to determine the individual author’s component in the meanings of keywords and describe their systemic relationships. The main research method is contextual analysis. In the process of identifying actualized semes in the structure of lexical meaning, the method of component analysis is used; when determining the subtlest semantic nuances associated with the peculiarities of the writer’s individual worldview, the semantic-stylistic method is used. Historical and literary articles and essays by E. Zamyatin became the source of factual material. The result of the analysis was a description of a significant fragment of his individual picture of the world. The study of the writer’s journalism in the proposed perspective was undertaken for the first time. As a result, a conclusion was made about the principles of constructing a lexical microsystem that reflects the dialectical views of E. Zamyatin, the contextual meanings of keywords were identified, and methods for updating these meanings were described. The principle of binary oppositions, which underlies the writer’s comprehension of reality, determined the specifics of his idiostyle: the use of language game techniques based on the opposition of the meanings of a polysemic word, the active use of contextual antonyms and occasional words.

A novel generalized adversarial image method using descriptive features

Abstract— Currently, machine learning not only solves simple problems such as object classification but also machine learning is widely applied in the field of computer vision such as identification systems, object detection, and modules in the authentication system, intelligent processing algorithms such as automatic driving, chatbot, etc. Deep learning models on GAN networks can automatically generate image data such as objects, animals, human faces, or the like by learning the word features of images in datasets such as MS-COCO, ImageNET, CUB, etc. Using this technique, attackers can fake images in some cases with malicious intent. In this paper, the authors propose to build a Generative Adversarial Network to create images that fool the target model YOLOv7, INCEPTIONv3. Experimental results on the CUB dataset show our proposed model's ability to generate adversarial examples is highly effective with an average image generation time equal to 0.16 seconds/an image. The successful rate of fooling the model reached over 85%, average recognition rate reached over 45% for the YOLOv7 model. When experimenting on the INCEPTIONv3 model, the successful rate of fooling the model reached over 95%, average recognition rate reached over 50%. The image fidelity evaluated by the PSNR index reached an average of greater than 29.

Lexical-semantic abilities of five-to-six-year-old children

<p><strong>Introduction.</strong> The development of the semantic level of the language system begins at the end of the first year of life, when a child begins to use the first words. During childhood, the child gradually increases his/her vocabulary and learns semantic characteristics. The main aim of this paper is to determine the lexical-semantic abilities of preschool children.</p> <p><strong>Methods.</strong> The sample included 50 children of both sexes, aged five to six years. According to age, the respondents were divided into two groups. The first group consisted of five-year-olds, and the second of six-year-olds. The Semantic Test and the Free Word Association Test were used in the research. The semantic test assessed the development of the following categories of words: homonyms, antonyms, synonyms, and metonyms. The free word association test was used to assess the types of responses after a given stimulus word.</p> <p><strong>Results.</strong> The results showed that six-year-olds had higher average values compared to five-year-olds on the total score, as well as on individual lexical categories of the Semantic Test. It was also shown that five-year-old girls had higher average values compared to boys of the same age on the Semantic Test. No statistically significant differences were found between five- and six-year-olds in terms of the representation of certain types of answers on the Free Word Association Test. As for sex, it was shown that on the Free Word Association Test, girls had statistically significantly higher number of missing answers compared to boys.</p> <p><strong>Conclusion.</strong> It was concluded that the lexical-semantic abilities of preschool children depended on their age, and that there was certain regularity in the development of the semantic features of words and the lexical-semantic structure</p>

Optimization of Translation Techniques between English and Chinese Literary Works in the Information Age

Abstract Under the background of cultural globalization, English-Chinese literary translation plays an important role in which it is not only a process of language conversion, but also a process of cultural conversion. In this paper, the BiGUR-LM-Attention optimization model is fused and constructed using the WordNet semantic similarity model, GRU-LM one-way gated similarity model, and BiGR-LM two-way gated similarity model. The LDA theme model is selected to generate the 3-layer Bayesian network structure of literary works’ paragraphs, themes and words to obtain the probability information that represents the highest attention of the work’s text theme, which constitutes the attention mechanism feature word vector. Finally, five classic literary works are selected as the training corpus to compare and analyze the translation quality between machine translation and human translation in the mutual translation of English and Chinese literary works. The results show that the number of errors and the total score of machine translation are 95 and 275, which are significantly lower than those of manual translation, 105.37 and 360.19. The new model has outstanding translation performance in semantic recognition, dialect, and special nouns, which effectively improves the translation quality of literary works and is of great significance for the dissemination of cultural works.

Fusion of the word2vec word embedding model and cluster analysis for the communication of music intangible cultural heritage

This article aims to propose a method for computing the similarity between lengthy texts on intangible cultural heritage (ICH), to facilitate the swift and efficient acquisition of knowledge about it by the public and promote the dissemination and preservation of this culture. This proposed method builds on traditional text similarity techniques. The ultimate goal is to group together those lengthy texts on ICH that exhibit a high degree of similarity. First of all, the word2vec model is utilized to construct the feature word vector of music ICH communication. This includes the acquisition of long text data on music ICH, the word segmentation of music ICH communication based on the dictionary method in the field of ICH, and the creation of a word2vec model of music ICH communication. A clustering algorithm analyzes and categorizes ICH communication within the music. This procedure involves employing text semantic similarity, utilizing a similarity calculation method based on optimized Word Mover Distance (WMD), and designing long ICH communication clustering. The main objective of this analysis is to enhance the understanding and classification of the intricate nature of ICH within the musical realm. Finally, experiments are conducted to confirm the model’s effectiveness. The results show that: (1) the text word vector training based on the word2vec model is highly accurate; (2) with the increase in K value, the effect of each category of intangible word vector is improving; (3) the final F1-measure value of the clustering experiment based on the optimized WMD is 0.84. These findings affirm the usefulness and accuracy of the proposed methodology.