English Sentences Research Articles

Субстантивные предложения как средство выражения британскими политиками суждений о своей идентичности (на материале стенограмм заседаний парламента Великобритании за 2020 г.)

The paper examines the semantic-structural characteristics of substantive sentences used by British politicians when verbalizing their identity judgements in parliamentary discourse. Attention is focused on the substantive sentences with the structure “S (Pr) + P (Vfcop + N)” implementing the proposition P(x) with the semantics “attribution of a feature to the subject of judgment”. The aim of the study is to determine what view of the speaker as an identity holder is formed by means of these sentences, depending on their semantic-structural properties. The scientific novelty of the study lies in the fact that it identifies and describes types of substantive sentences that differ in their semantic-structural characteristics and reveals their correlation with the verbalization of certain information about the speaker’s identity. As a result of the study, it is found that in English substantive sentences of different types a different format of the speaker’s perception is created: the speaker can act as a representative of a certain category (group) of people, as a single whole with a community of people sharing the same characteristics, as an owner of rather vague properties and characteristics inherent in all members of the group to which the speaker himself belongs.

3DGEN: a framework for generating custom-made synthetic 3D datasets for civil structure health monitoring

The availability of high-quality datasets is increasingly critical in the field of computer vision-based civil structural health monitoring, where deep learning approaches have gained prominence. However, the lack of specialized datasets for such tasks poses a significant challenge for training a reliable model. To address this challenge, a framework, 3DGEN, is proposed to swiftly generate realistic synthetic 3D datasets which can be targeted for specific tasks. The framework is based on diverse 3D civil structural models, rendering them from various angles and providing depth information and camera parameters for training neural networks. By employing mathematical methods, such as analytical solutions and/or numerical simulations, deformation of civil engineering structures can be generated, ensuring a reliable representation of their real-world shapes and characteristics in the 3D datasets. For texture generation, a generative 3D texturing method enables users to specify desired textures using plain English sentences. Two successful experiments are conducted to (1) assess the efficiency of generating the 3D datasets using two distinct structures, (2) train a monocular depth estimation network to perform 3D surface reconstruction with the generated dataset. Notably, 3DGEN is not limited to 3D surface reconstruction; it can also be used for training neural networks for various other tasks. The code and dataset are available at: https://github.com/YANDA-SHAO/Beam-Dataset-SE

English Sentiment Analysis and its Application in Translation Based on Decision Tree Algorithm

Sentimental analysis belongs to the class of Natural Language Processing (NLP) based on the rule and machine model. The proposed model comprises of the pre-defined function for the estimation of the features in the English statements. This paper presents the Reflect Sentiment Translation Decision Tree (RSTDT), a novel model designed to integrate sentiment analysis and translation tasks for English text. The RSTDT model combines the strengths of decision tree algorithms with feature extraction techniques to accurately analyze sentiment and translate text across languages. The proposed RSTDT dataset comprises English sentences with annotated sentiment labels, the RSTDT model is trained to identify sentiment polarity and generate corresponding translations in Arabic. The proposed RSTDT model uses Traslation mapping for the estimation of the sentimental features. In order to estimate and classify the features in the neural network, the processes features are assessed using the decision tree model. The RSTDT model's efficacy in precisely capturing sentiment nuances and generating linguistically appropriate translations was shown through thorough testing and review. The model achieves high accuracy in sentiment analysis and exhibits proficiency in translating sentiment-rich content into Arabic while maintaining contextual relevance. Additionally, robust classification performance metrics underscore the model's efficacy in accurately classifying English words into sentiment categories. The RSTDT model offers a promising solution for multilingual sentiment analysis applications, with potential applications in social media monitoring, customer feedback analysis, and cross-cultural sentiment analysis.

Research on Multimodal College English Teaching Model Based on Genetic Algorithm

Analyzing College English texts is essential for quantitatively evaluating their grammar, phrases, and words to enhance their use in writing, conversation, and other contexts. The precise and clear use of College English words, phrases, and sentences is essential to knowledge-based and foundational learning systems. Text data analytics run into problems with data amount, data diversity, data integration and interoperability. It is challenging to accomplish human-computer interaction in spoken College English communication and to assist students with corrections using the conventional methodology of teaching College English. Therefore, this paper proposed the Genetic Algorithm-based intelligent English course optimization system (GA-IECOS) to handle the scheduling above issue of college English classes and optimize college English teaching courses. The results demonstrate that the conventional BP neural network's local scheduling optimization issue may be resolved using the multidirectional mutation genetic BP neural network method. Subsequently, a mix of formative and summative assessments will be used to establish a couple of groups to evaluate the effectiveness using a control population and a trial group of a GA-IECOS for English language classes using a multidirectional mutation genetic algorithm and an optimization neural network. The results demonstrate that the GA-IECOS algorithm is more effective in the classroom and may greatly improve students' English performance

Intelligibility of Natively and Nonnatively Produced English Speech Presented in Noise to a Large Cohort of United States Service Members.

A corpus of English matrix sentences produced by 60 native and nonnative speakers of English was developed as part of a multinational coalition task group. This corpus was tested on a large cohort of U.S. Service members in order to examine the effects of talker nativeness, listener nativeness, masker type, and hearing sensitivity on speech recognition performance in this population. A total of 1,939 U.S. Service members (ages 18-68 years) completed this closed-set listening task, including 430 women and 110 nonnative English speakers. Stimuli were produced by native and nonnative speakers of English and were presented in speech-shaped noise and multitalker babble. Keyword recognition accuracy and response times were analyzed. General(ized) linear mixed-effects regression models found that, on the whole, speech recognition performance was lower for listeners who identified as nonnative speakers of English and when listening to speech produced by nonnative speakers of English. Talker and listener effects were more pronounced when listening in a babble masker than in a speech-shaped noise masker. Response times varied as a function of recognition score, with longest response times found for intermediate levels of performance. This study found additive effects of talker and listener nonnativeness when listening to speech in background noise. These effects were present in both accuracy and response time measures. No multiplicative effects of talker and listener language background were found. There was little evidence of a negative interaction between talker nonnativeness and hearing impairment, suggesting that these factors may have redundant effects on speech recognition. https://doi.org/10.23641/asha.26060191.

DIFFICULTIES OF TRANSLATING POLYSEMOUS LEXICAL ITEMS AND THE STRATEGIES ADOPTED: A CASE STUDY OF EFL LEARNERS AT SABER FACULTY OF SCIENCE AND EDUCATION – DEPARTMENT OF ENGLISH - UNIVERSITY OF LAHIJ

This study aimed to investigate the knowledge of the undergraduate students in translating three Arabic polysemous lexical items into English in different contexts. The research aimed to assess the student's proficiency in giving the meaning of polysemous words and measure their ability to select appropriate equivalents as well as the strategies adopted. The study involved thirty students from the Department of English, Saber Faculty of Science and Education. The participants carried out a translation test that contextualized three Arabic lexical polysemous lexical items (ضرب - حصر- تخفيف) within fifteen sentences. Statistical analysis of the data revealed a lack of knowledge among undergraduate students as they faced difficulties in constructing proper sentence patterns and a lack of competence in finding the exact English equivalents, resulting in challenges in selecting words that formed coherent English sentences and violating the sentence structure. Consequently, their production of correct English sentences was compromised. Furthermore, the study identified that the students relied on various language strategies, including L1 transfer, synonymy, avoidance, and paraphrasing, to facilitate their translation process. However, these strategies led to unsatisfactory English translations.

모바일 앱을 활용한 영어학습이 산업대학 학생들에게 미치는 학습효과

This study examines the impact of utilizing a mobile application(App) for English learning on students at an industrial university. Forty-five participants in an English conversation course took the class first and used the ClassCard app to learn English sentences every week during a semester. This study conducted pre- and post-tests of English proficiency to analyze the results and a survey at the end of the semester to examine the impact of utilizing the App. As a result, first, this study found that participants’ achievement scores were significantly higher in the post-test compared to the pre-test. It showed that the participants’ performance improved because they were able to make the most of their abilities using the apps while managing their careers. Second, participants were found to exhibit three types of study habits: self-directed learning, repetitive learning, and willingness to use spare time for studying. Participants also responded that they preferred activities that allowed them to learn English sentences while understanding them step by step and practicing them through fun games. This study also found that there was a demand for features such as AI-based conversation practice. Finally, this study offers suggestions for enhancing the English communication abilities of industrial university students.

A data-efficient and easy-to-use lip language interface based on wearable motion capture and speech movement reconstruction.

Lip language recognition urgently needs wearable and easy-to-use interfaces for interference-free and high-fidelity lip-reading acquisition and to develop accompanying data-efficient decoder-modeling methods. Existing solutions suffer from unreliable lip reading, are data hungry, and exhibit poor generalization. Here, we propose a wearable lip language decoding technology that enables interference-free and high-fidelity acquisition of lip movements and data-efficient recognition of fluent lip language based on wearable motion capture and continuous lip speech movement reconstruction. The method allows us to artificially generate any wanted continuous speech datasets from a very limited corpus of word samples from users. By using these artificial datasets to train the decoder, we achieve an average accuracy of 92.0% across individuals (n = 7) for actual continuous and fluent lip speech recognition for 93 English sentences, even observing no training burn on users because all training datasets are artificially generated. Our method greatly minimizes users' training/learning load and presents a data-efficient and easy-to-use paradigm for lip language recognition.

The Correlation between The Vocabulary Mastery and Writing Skills of the Ninth Graders of SMP Negeri 1 Bandongan

Four language skills are needed to master a language: listening, reading, speaking, and writing. Two aspects are needed to communicate in written form: vocabulary mastery and writing skills. Someone needs writing skills and extensive vocabulary knowledge to create good writing. Even so, it was found that there were still students who had difficulties in translating words from Indonesian to English and vice versa, challenges in using the right words, and problems in constructing sentences in English. In addition, some students still have limited knowledge of vocabulary. This study focused on finding the students’ vocabulary mastery level, writing skills level, and the correlation between vocabulary mastery and writing skills. A person will master four language skills, including writing, easier if they have a broad vocabulary. The more comprehensive the vocabulary knowledge, the more vocabulary will be used in writing; hence, the vocabulary used in the writing will be more varied. Correlation research was used in this study. The study population was 9th graders of SMP Negeri 1 Bandongan, and the sample was 29 students from class 9B at SMP Negeri 1 Bandongan. The study instruments were a vocabulary test and a writing test. The instruments were derived from previous research. The data taken was analyzed using a correlation test with SPSS. The results of this study show no significant correlation between vocabulary mastery and writing skills with the Sig value. (2-tailed) 0.145 > 0.05. Furthermore, the correlation coefficient is 0.278. Hence, the correlation between vocabulary mastery and writing skills was low. This study suggests that teachers and students focus on learning and mastering vocabulary and other aspects that influence writing ability and the writing itself.

EXPLORING THE ACADEMIC NEEDS OF RURAL UNIVERSITY STUDENTS IN PRE-INTERMEDIATE GRAMMAR COURSE

In order to assist lecturers in customising their teaching strategies and course materials for pre-intermediate grammar students living in rural locations, this study focuses on doing pre-lecture needs analysis. Using a quantitative method, 24 students enrolled in Universitas Sulawesi Barat's English Language Education programme for the academic year 2023–2024 were given closed-ended questions via a WhatsApp group. To find patterns in the gathered data, statistical analysis and purposeful sampling were applied. The results shed light on a variety of student characteristics, including age, mother tongue distribution, performance in the basic grammar course, ability to build simple English sentences, mastery of verb tense formulas, lexical proficiency, preferences for learning and assignments, and skill targets. The study draws attention to a variety of factors, including student skill objectives, favourite activities, assignment preferences, performance gaps, and vocabulary problems. Teachers and policymakers can improve the quality of English language instruction, particularly in grammar classes, and establish a supportive learning environment for students in Indonesia's rural areas by acknowledging and addressing the issues and preferences that have been identified. Furthermore, teachers around the world can use the needs analysis tool, the WhatsApp polling technique, and the pre-lecture activities as a source of inspiration and guidance to deliver engaging, balanced, and professional lectures that are customised to the needs of their students and meet graduate competency standards.

Jugendsprache: bahasa remaja Jerman pada akun Instagram Funk

German youth language or Jugendsprache is a language style used by German teenagers and young adults in their daily communication. This language style is adopted by a German content network named Funk in its Instagram account posts to gain attention from its audience, which specifically targets people aged between 14 to 29 years old. The purpose of this research is to investigate the forms of Jugendsprache used by Funk in their Instagram posts. The design of this research is descriptive qualitative. The data corpus in total of 288 sentences was acquired from Funk’s Instagram account. The results unveiled that there were 177 lexicons, 55 word formations and 28 semantic shifts found from the data. Anglicism, in total of 112 data, is the most dominant lexicon phenomenon compared to the others. Another significant process are 29 composition data in word formation and 23 extensions of meaning data in semantic shift. From this research, it was found that English is widely used in Funk’s contents, either in the form of words, phrases, sentences in full English or English words adopted into the German morphological system. Funk utilizes English to make their contents interesting for its’ target audience, the young people.

Azərbaycan Dövlət Pedaqoji Universitetinin Xarici Dillər Mərkəzi

The article talks about different models of simple sentences in English, the role and importance of simple sentences in the formation of language skills of learners starting from school education, models of developing language knowledge and techniques and methods to be used to express ideas freely and speak comfortably. Author role-playing games, life stories, creating sentences, picture supports, etc. considers the role of forming the habit of forming simple sentences using methods useful in teaching simple sentences in English and improving language skills. In the article, the author also refers to the research and talks about the ways of translating simple sentences in English into Azerbaijani, and appreciates the role of this procedure in the formation of language skills in learners.

Lesion-symptom Mapping of Acceptability Judgments in Chronic Poststroke Aphasia Reveals the Neurobiological Underpinnings of Receptive Syntax.

Disagreements persist regarding the neural basis of syntactic processing, which has been linked both to inferior frontal and posterior temporal regions of the brain. One focal point of the debate concerns the role of inferior frontal areas in receptive syntactic ability, which is mostly assessed using sentence comprehension involving complex syntactic structures, a task that is potentially confounded with working memory. Syntactic acceptability judgments may provide a better measure of receptive syntax by reducing the need to use high working memory load and complex sentences and by enabling assessment of various types of syntactic violations. We therefore tested the perception of grammatical violations by people with poststroke aphasia (n = 25), along with matched controls (n = 16), using English sentences involving errors in word order, agreement, or subcategorization. Lesion data were also collected. Control participants performed near ceiling in accuracy with higher discriminability of agreement and subcategorization violations than word order; aphasia participants were less able to discriminate violations, but, on average, paralleled control participants discriminability of types of violations. Lesion-symptom mapping showed a correlation between discriminability and posterior temporal regions, but not inferior frontal regions. We argue that these results diverge from models holding that frontal areas are amodal core regions in syntactic structure building and favor models that posit a core hierarchical system in posterior temporal regions.

Contrastive Analysis of Adjectives in English and Idọma Sentence Structures

English Language and its structural patterns share both similar and different features when contrasted with other languages. In this paper, the syntactic patterns of modification of the Noun Phrase (NP), a term rooted in Chomsky’s grammatical category, are examined in English and Idọma languages. The aim is to highlight potential implications for the Idọma learners of English language as a second language. Using Government and Binding theory for data analysis, the study draws insights from Contrastive Analysis. To provide data for analysis, two episodes of Okonone, a radio programme presented in Idọma and aired on Joy FM, are recorded and transcribed. Findings reveal that both languages have predicative adjectives and they function as subject and object complements. Adjectives can also occur after the indefinite pronouns in both English and Idọma sentence structures. Unlike in English language, adjectives are observed to predominantly occur post-nominally in Idọma Syntax. Similarly, while nouns can be used as adjectives in the English sentence structure, Idọma language does not provide for such a usage. To compare adjectives in English, standard morphological processes allow users to generate single words. Since Idọma speakers rely heavily on post-nominal modification, comparison is often embedded within the adjectival structure using two or three words. The study concludes that these similarities aid smooth learning of the English language while the dissimilarities impede the proficiency of Idọma learners of the English language.

Visual scanning patterns of a talking face when evaluating phonetic information in a native and non-native language.

When comprehending speech, listeners can use information encoded in visual cues from a face to enhance auditory speech comprehension. For example, prior work has shown that the mouth movements reflect articulatory features of speech segments and durational information, while pitch and speech amplitude are primarily cued by eyebrow and head movements. Little is known about how the visual perception of segmental and prosodic speech information is influenced by linguistic experience. Using eye-tracking, we studied how perceivers' visual scanning of different regions on a talking face predicts accuracy in a task targeting both segmental versus prosodic information, and also asked how this was influenced by language familiarity. Twenty-four native English perceivers heard two audio sentences in either English or Mandarin (an unfamiliar, non-native language), which sometimes differed in segmental or prosodic information (or both). Perceivers then saw a silent video of a talking face, and judged whether that video matched either the first or second audio sentence (or whether both sentences were the same). First, increased looking to the mouth predicted correct responses only for non-native language trials. Second, the start of a successful search for speech information in the mouth area was significantly delayed in non-native versus native trials, but just when there were only prosodic differences in the auditory sentences, and not when there were segmental differences. Third, (in correct trials) the saccade amplitude in native language trials was significantly greater than in non-native trials, indicating more intensely focused fixations in the latter. Taken together, these results suggest that mouth-looking was generally more evident when processing a non-native versus native language in all analyses, but fascinatingly, when measuring perceivers' latency to fixate the mouth, this language effect was largest in trials where only prosodic information was useful for the task.

Examining Cross-linguistic Influence of Japanese Word Order: An Eye-Tracking Study on L2 English Learners’ Text Comprehension

Cross-linguistic influence is a systematic and unavoidable feature of language learning. This study aimed to investigate whether prepositive word order in Japanese noun clauses can serve as a cross-linguistic influence element in the acquisition of relative clauses (a form of a postpositive modifier) in English. To test this hypothesis, this study conducted an eye-tracking experiment with native Japanese speakers and showed the following results statistically. First, Japanese participants generally paid more attention to modifier clauses than the noun phrases they modified when reading English sentences with relative clauses. Second, when interpreting accusative relative clauses, longer fixations were observed both in the modifier clauses and in the antecedents, likely due to their complexity for Japanese L2 learners of English, as evidenced by the lowest accuracy rate. Finally, and most notably, the findings revealed that the participants who had not fully acquired the relative clause construction focused more on the left side of the relative pronoun than those who had. This tendency was most prominent when interpreting subjective relative clauses, a result that supports the hypothesis that prepositive L1 modifiers could be a CLI element in the acquisition of English postpositive modifiers.

Morphosyntactic agreement in English: does it help the listener in noise?

English morphosyntactic agreement, such as determiner–noun agreement in These cabs broke down and noun–verb agreement in The cabs break down, has a few interesting properties that enable us to investigate whether agreement has a psycholinguistic function, that is, whether it helps the listener process linguistic information expressed by a speaker. The present project relies on these properties in a perception experiment, examines the two aforementioned types of English agreement, and aims at analyzing whether and how native English listeners benefit from agreement. The two types of agreement were contrasted with cases without any overtly agreeing elements (e.g. The cabs broke down). Native speakers of English with normal hearing heard short English sentences in quiet and in more or less intense white noise and were requested to indicate whether the second word of the sentence (e.g. These cabs broke down) was a singular or plural noun. Accuracy was entered as the response variable in the binomial logistic regression model. Results showed that overt determiner–noun agreement clearly increased response accuracy, while noun–verb agreement had at best marginal effects. The findings are interpreted against the background of functional aspects of linguistic structures in English, in the context of unfavorable listening conditions in particular.

Impact of Japanese L1 Rhythm on English L2 Speech.

The study aimed to examine whether L1 speech rhythm affects L2 speech by assessing how the speech rhythm of Japanese L2 English speakers differed from native speakers. We chose Japanese and English because they differ markedly in the phonological properties that likely contribute to speech rhythm. Speech rhythm was measured by the variability of vowel and consonant intervals using rate-normalized rhythm metrics (VarcoV and VarcoC; nPVI-V and nPVI-C) and %V. The study utilized recordings of spoken sentences in English by 10 native Australian English speakers; and in English and also in Japanese by 10 native Japanese speakers (who had limited experience in speaking English). Experiment 1 compared the rhythm of L1 English (by measuring 1,750 vowels and 3,093 consonants from 20 sentences) and L1 Japanese (1,923 vowels and 2,097 consonants from 10 sentences). The results showed that for all measures, Japanese had reduced durational variability in both consonant and vowel intervals compared with English. In Experiment 2, we examined the rhythm characteristics of L1 and L2 English using 40 sentences (including the 20 in Experiment 1). The results showed that vowel and consonant intervals were less variable in L2 (Japanese English) than in L1 (Australian English) speech, mirroring the results of Experiment 1. Overall, the results are consistent with the proposal that L1 (Japanese) speech rhythm influenced L2 (English) speech.

英语被动句的汉译探析

In the academic research and practice of English translation, in-depth analysis and discussion on English passive sentences and their Chinese translation strategies are of great significance. This article employs a variety of Chinese translation strategies including direct conversion, active sentence conversion, addition, omission, and segmentation to unveil and analyze the essential factors that must be considered in the process of translating English passive sentences into Chinese. These factors encompass word class, grammatical structure, and specific language habits, underscoring their importance in ensuring the accuracy and fluency of translations. This paper aims to assist scholars and learners in the field of English translation not only to gain a deeper understanding of the grammatical and semantic characteristics of English passive sentences but also to master and apply effective translation techniques to enhance their ability to accurately translate English passive sentences into Chinese.

Application of an adaptive genetic algorithm and machine learning in an English text analysis teaching system

English text analysis is required for quantitative grammar, phrase, and word assessment to improve its usage in conversation, drafting, etc. In particular, a teaching system requires the flawless and precise use of English words, phrases, and sentences for fundamental and knowledge-based learning. Data integration and interoperability, data volume, and data variety pose difficulties for text data analytics. This article discusses a heterogeneous English teaching system text analysis solution that integrates a Genetic Algorithm (GA) and Deep Learning (DL). The Text Analytical Model (TAM) uses fused methods (FM) to handle words and their placement for sentence framing. The framed teaching sentence is analyzed lexically for its precision and meaning with conventional features. Initially, the possible word combinations using the crossover and mutation operations of the genetic process are performed. The outcome of the genetic process forecasts different possible sentence combinations for delivering the English context to students. The mutation process identifies the most precise lexical sentence that fits the subject and context. Based on precision, the DL model is trained to reduce the initial population of the GA process; this is achieved in English teaching through repetitions or drilling performed for different sentences and words. The learning converges towards precision in delivering context-based words and sentences by reducing unnecessary crossovers in the genetic process to reduce computational complexity. This feature, therefore, achieves high-precision convergence with less computation time compared to methods of the same kind. TAM-FM improves the precision convergence, forecast probability, and population refinement by 9.5%, 11.39%, and 8.81%, respectively. TAM-FM reduces the computation time and complexity by 9.67% and 8.3%, respectively.