Short Sentences Research Articles

Disability does not negatively impact linguistic visual-spatial processing for hearing adult learners of a signed language

The majority of adult learners of a signed language are hearing and have little to no experience with a signed language. Thus, they must simultaneously learn a specific language and how to communicate within the visual-gestural modality. Past studies have examined modality-unique drivers of acquisition within first and second signed language learners. In the former group, atypically developing signers have provided a unique axis—namely, disability—for analyzing the intersection of language, modality, and cognition. Here, we extend the question of how cognitive disabilities affect signed language acquisition to a novel audience: hearing, second language (L2) learners of a signed language. We ask whether disability status influences the processing of spatial scenes (perspective taking) and short sentences (phonological contrasts), two aspects of the learning of a signed language. For the methodology, we conducted a secondary, exploratory analysis of a data set including college-level American Sign Language (ASL) students. Participants completed an ASL phonological- discrimination task as well as non-linguistic and linguistic (ASL) versions of a perspective-taking task. Accuracy and response time measures for the tests were compared between a disability group with self-reported diagnoses (e.g., ADHD, learning disability) and a neurotypical group with no self-reported diagnoses. The results revealed that the disability group collectively had lower accuracy compared to the neurotypical group only on the non-linguistic perspective-taking task. Moreover, the group of students who specifically identified as having a learning disability performed worse than students who self-reported using other categories of disabilities affecting cognition. We interpret these findings as demonstrating, crucially, that the signed modality itself does not generally disadvantage disabled and/or neurodiverse learners, even those who may exhibit challenges in visuospatial processing. We recommend that signed language instructors specifically support and monitor students labeled with learning disabilities to ensure development of visual-spatial skills and processing in signed language.

Identifying Reading Fluency in Pupils with and without Dyslexia Using a Machine Learning Model on Texts Assessed with a Readability Application

Measurement of readability is an important tool for assessing reading disorders such as dyslexia. Among the screening procedures for dyslexia is the reading fluency test, which is defined as the ability to read with speed, accuracy and proper expression. The reading fluency test often consists of a sequence of unrelated written texts ranging from simple short sentences to more difficult and longer paragraphs. In psychological testing instruments, subjective text assessment is often replaced by objective readability formulas, e.g., the Automated Readability Index. Readability formulas extract multiple features from a given text and output a score indicating the difficulty of the text. The aim of the present study is to build a machine learning model that discriminates between pupils identified with dyslexia and a control group without dyslexia based on fluency in oral reading of texts assessed with a readability application developed within the project For the Quality of Slovenian Textbooks. We focus on differentiation between both groups of pupils by analysing data obtained from transcriptions of audio recordings of oral reading. The empirical study was conducted with 27 pupils aged 8 and 9 with officially diagnosed dyslexia and a control group without identified dyslexia.

Classifying handwriting samples according to their type using discriminant analysis

Classifying handwriting samples according to their type (i.e. natural, disguised, traced, simulated or unintentionally unnatural) is an important task in handwriting analysis. It may facilitate the collection of writing standards and also help experts to assess the differences between questioned material and comparative samples or to choose the best writing features and the most relevant examination protocol for the case. Current research aimed to create a method for classifying the type of a handwriting sample using discriminant analysis. Five basic types (i.e. natural, disguised, traced, simulated and unintentionally unnatural) and some subtypes were included in this study. Participants (N = 139) wrote their full signatures, fictional signatures or a short sentence. Motor and dimensional features were assessed. The methods proved to be more than twice as accurate in classifying samples according to their type than a random choice probability (e.g. 44% as opposed to 17% for the 6-types classifier). This proof-of-a-concept study demonstrates that handwriting samples may be classified according to their type with satisfying accuracy based on their writing features and statistical tools of discriminant analysis. However, further studies are necessary to improve the accuracy of the method.

How Do Foreign Language Learners Process L2 Emotion Words in Silent Reading? An Eye-Tracking Study

The current study aimed to examine the processing of emotion words in L2 silent reading. We conducted two experiments in which Arab learners of English as a foreign language (EFL) read short English sentences in which target words were embedded. The participants’ eye movements were recorded and analyzed. The results of Experiment 1, which compared the processing of emotionally positive versus neutral words by 44 participants, did not reveal any significant effect for word type. The results only showed a few instances of significant interactions between word type and word frequency (i.e., positive words were read faster than neutral words only in the case of high-frequency words) and arousal (i.e., positive words were recognized faster than neutral words only when the target words were low in arousal). The results of Experiment 2, which compared the processing of emotionally negative versus neutral words by 43 participants, only established one effect of word type on the skipping rate which was also modulated by length (i.e., negative words were less likely to be skipped, particularly shorter ones). Moreover, arousal interacted with word type (i.e., only the negative words with low arousal were read faster than neutral words in two eye-movement measures).

Visual biases in evaluation of speakers' and singers' voice type by cis and trans listeners.

A singer's or speaker's Fach (voice type) should be appraised based on acoustic cues characterizing their voice. Instead, in practice, it is often influenced by the individual's physical appearance. This is especially distressful for transgender people who may be excluded from formal singing because of perceived mismatch between their voice and appearance. To eventually break down these visual biases, we need a better understanding of the conditions under which they occur. Specifically, we hypothesized that trans listeners (not actors) would be better able to resist such biases, relative to cis listeners, precisely because they would be more aware of appearance-voice dissociations. In an online study, 85 cisgender and 81 transgender participants were presented with 18 different actors singing or speaking short sentences. These actors covered six voice categories from high/bright (traditionally feminine) to low/dark (traditionally masculine) voices: namely soprano, mezzo-soprano (referred to henceforth as mezzo), contralto (referred to henceforth as alto), tenor, baritone, and bass. Every participant provided voice type ratings for (1) Audio-only (A) stimuli to get an unbiased estimate of a given actor's voice type, (2) Video-only (V) stimuli to get an estimate of the strength of the bias itself, and (3) combined Audio-Visual (AV) stimuli to see how much visual cues would affect the evaluation of the audio. Results demonstrated that visual biases are not subtle and hold across the entire scale, shifting voice appraisal by about a third of the distance between adjacent voice types (for example, a third of the bass-to-baritone distance). This shift was 30% smaller for trans than for cis listeners, confirming our main hypothesis. This pattern was largely similar whether actors sung or spoke, though singing overall led to more feminine/high/bright ratings. This study is one of the first demonstrations that transgender listeners are in fact better judges of a singer's or speaker's voice type because they are better able to separate the actors' voice from their appearance, a finding that opens exciting avenues to fight more generally against implicit (or sometimes explicit) biases in voice appraisal.

CATEGORY OF TENSION IN J.D. SALINGER’S WORKS

The purpose of the article is to identify category of tension in J.D.Salinger’s works. There are two main research trends of category of tension: category of tension of existence by A.F.Losev’s philosophy; a linguistic nature of tension category by T.V.Yudina. The representational method let reveal a plan of content and expression of the stories. In «Uncle Wiggily in Connecticut» and «The Laughing Man» means of tense category representation are identified on different levels: lexical – slangs, vulgar words, author’s words; syntactical – repetitions, short imperative sentences; stylistic – epithets; a sublime tone of the narration.

The Exploratory Study on the Development of Online Based Auditory Training Contents: Based on the Analysis of the Application for Auditory-Training for the Elderly in their 70s and Older

Purpose: Due to the recent COVID-19 pandemic, the importance of teleservices such as telemedicine and digital treatment is increasing. Interest in teleservice is also growing in the filed of audiology. The increase in the demand for remote hearing services will likely lead to the need for online-based auditory training programs. This study aims to investigate and analyze the usage behaviors and functions, the satisfaction of the developed online-based auditory training program, and to suggest the implications of development directions for the online-based auditory training program.Methods: Individual in-depth interviews were conducted. Eleven participants, including five 70s~80s elderly hearing aids users, two program developers, three audiologists, and one professor of audiology, were recruited.Results: Three themes emerged from the interviews were; analysis contents in online-based auditory training, maximized ease of voluntary auditory training, and individualized contents required for the auditory training.Conclusion: The study results highlighted that online-based auditory training needs familiar, easy, short, technology strategy for elderly users with hearing loss. First, familiar topics greatly benefit elderly users because they do not want to learn new and diverse issues. Also, there was a need to provide contents that could attract individual interest using advanced technology. Second, short sentences must be quickly visualized to encourage participantation in the auditory training. Third, ease accessibility to the system for auditory training program is needed. Lastly, the elderly users were proud to use new technology for auditory training, so the professionals must encourage them to continue it. In addition, professionals pointed out that qualified professionals provide the auditory training service and need government support, such as voucher service, for the auditory training. These outcomes will contribute to refining the devleoped online-based auditory training program.

SIMPLE SENTENCE IN A·WE

A·we is the standard variety of the A·chik language also known as A·chikku which is also commonly known as the Garo language. The Garo language belongs to the Tibeto-Burman branch of the Sino-Tibetan language family. Since Syntax involves arranging words to create logical phrases, clauses, and sentences, simple sentence is an important part of syntax and thus knowing about the simple sentence in A·we forms the basis of writing correct sentences in this variety of the Garo language. Noam Chomsky, the famous Linguist used the phrase, “ colorless green ideas sleep furiously’ in his book, Syntactic Structures (1957) as an example of a sentence which is syntactically and grammatically correct because it has the correct word order and the verb is consistent with the subject but is semantically incorrect. Chomsky (1957) thus illustrates that the rules governing syntax are different from the meaning conveyed by words. We can observe that there are subject and predicate in a simple sentence in language, which is the same for A·we. It is not essential that a simple sentence must be a short sentence and it is also possible to write a simple sentence if there is only one predicate used with a number of subjects to make a long sentence. Such sentences are still called a simple sentence. In this paper, we will discuss some classification of simple sentence in A·we which will further contribute to the study of syntax in A·we as well as aid in constructing proper sentences in the language.

Less is more in language production: an information-theoretic analysis of agrammatism in primary progressive aphasia.

Agrammatism is a disorder of language production characterized by short, simplified sentences, the omission of function words, an increased use of nouns over verbs and a higher use of heavy verbs. Despite observing these phenomena for decades, the accounts of agrammatism have not converged. Here, we propose and test the hypothesis that the lexical profile of agrammatism results from a process that opts for words with a lower frequency of occurrence to increase lexical information. Furthermore, we hypothesize that this process is a compensatory response to patients' core deficit in producing long, complex sentences. In this cross-sectional study, we analysed speech samples of patients with primary progressive aphasia (n = 100) and healthy speakers (n = 65) as they described a picture. The patient cohort included 34 individuals with the non-fluent variant, 41 with the logopenic variant and 25 with the semantic variant of primary progressive aphasia. We first analysed a large corpus of spoken language and found that the word types preferred by patients with agrammatism tend to have lower frequencies of occurrence than less preferred words. We then conducted a computational simulation to examine the impact of word frequency on lexical information as measured by entropy. We found that strings of words that exclude highly frequent words have a more uniform word distribution, thereby increasing lexical entropy. To test whether the lexical profile of agrammatism results from their inability to produce long sentences, we asked healthy speakers to produce short sentences during the picture description task. We found that, under this constrained condition, a similar lexical profile of agrammatism emerged in the short sentences of healthy individuals, including fewer function words, more nouns than verbs and more heavy verbs than light verbs. This lexical profile of short sentences resulted in their lower average word frequency than unconstrained sentences. We extended this finding by showing that, in general, shorter sentences get packaged with lower-frequency words as a basic property of efficient language production, evident in the language of healthy speakers and all primary progressive aphasia variants.

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.

The Influence of Focus on the Activation of Alternatives in Speech Production—An Online Picture-Word-Interference Experiment

In previous picture naming tasks, semantically related distractor words (co-hyponyms to the target word) induce interference, which is usually taken as evidence for lexical competition. In an online picture-word-interference experiment, we showed that distractor words that share a feature with the target (here: their natural prototypical color), also induced interference. Pictures were not named with single words but with short descriptive sentences (“The heart is red”). Focus on the noun modulated the interference effect. In particular, when target and distractor were presented simultaneously, the interference effect was significantly reduced in the narrow focus condition, compared to broad focus. We discuss our findings for focus production against the findings on language comprehension reported in the literature, which mostly observed facilitatory effects of focus marking on the comprehension of focus alternatives.

SCF‐ChemBio: Chemical Biology Tour de France

Chemical biology is a steadily growing field that has traditionally struggled to clearly define its boundaries in a short sentence. However, it can be stated that through the development of chemical and physicochemical tools, concepts and methods, chemical biology aims to address or stimulate biological questions at the molecular level in living organisms. Chemical biologists design and develop molecular tools that can probe or modulate biological processes, in order to understand their function, and sometimes to modify it for specific applications, but also to observe and analyze these tools in complex biological environments. Essentially positioned as a fundamental approach, chemical biology often remains very close to potential applications as it builds molecular objects capable of reacting to a significant biological stimulus. Chemical biology therefore finds natural development in fields such as health for the design of drugs and diagnostic systems or the environment for applications in crop science and ecology.

A case study on the evaluation of Korean-English legal translations by generic and custom neural machine translation engines

This paper addresses the quality of Korean-English legal translation outputs generated by three neural machine translation engines, namely NMT1, NMT2 and NMT3. NMT1 and NMT2 are widely available multilingual generic machine translation engines, whereas NMT3 is a custom machine translation engine for legal translation. In both automatic and human evaluations of the three engines’ English translations of Korean statutes, the custom engine outperformed the other two NMTs. BLEU and METEOR scores revealed that NMT3’s output was more similar to human reference translation. Human evaluation results confirmed NMT3’s strong performance, outperforming the generic engines in accuracy and fluency while underperforming in terminology. However, further analysis revealed that NMT3 was superior in handling terms comprising compound nouns and sentences over 25 words. Although the custom engine’s performance still requires substantial human involvement for quality translation, it showed a potential for the legal domain, demonstrating human parity in short sentences. The growing demand for speedy translation under budget constraints calls for further research on technology-based translation tools and human-computer interaction for legal translation.

Neural Correlates of Voice Learning with Distinctive and Non-Distinctive Faces.

Recognizing people from their voices may be facilitated by a voice's distinctiveness, in a manner similar to that which has been reported for faces. However, little is known about the neural time-course of voice learning and the role of facial information in voice learning. Based on evidence for audiovisual integration in the recognition of familiar people, we studied the behavioral and electrophysiological correlates of voice learning associated with distinctive or non-distinctive faces. We repeated twelve unfamiliar voices uttering short sentences, together with either distinctive or non-distinctive faces (depicted before and during voice presentation) in six learning-test cycles. During learning, distinctive faces increased early visually-evoked (N170, P200, N250) potentials relative to non-distinctive faces, and face distinctiveness modulated voice-elicited slow EEG activity at the occipito-temporal and fronto-central electrodes. At the test, unimodally-presented voices previously learned with distinctive faces were classified more quickly than were voices learned with non-distinctive faces, and also more quickly than novel voices. Moreover, voices previously learned with faces elicited an N250-like component that was similar in topography to that typically observed for facial stimuli. The preliminary source localization of this voice-induced N250 was compatible with a source in the fusiform gyrus. Taken together, our findings provide support for a theory of early interaction between voice and face processing areas during both learning and voice recognition.

Advertising Translation in the Perspective of Functional Equivalence Theory

With the development of China's national economy, the middle class has expanded and consumer power has increased. Especially in recent years, China's demand for international luxury brands is also on the rise. The next ad text can be seen everywhere in your daily life and is also one of the longest text types handled by professional translators in real work. This study uses luxury brand ad copy as a corpus based on the functional equivalence theory of translation and adopts a case analysis method, and this can be found by analyzing target consumer groups, brand positioning and stories. of the brand of each luxury brand. The original text emphasizes the attractiveness and appeal of the brand, highlights the consumer's purpose and suggests an adaptive translation strategy. Through the practice of translation, the translator discovered that the author of the original ad copy used positive vocabulary-level adjectives to highlight the merits of the product and attract customers. In the case of sentence patterns, based on the purpose of each module to convey information, relatively high-pitched long sentences are skillfully used to reflect the elegant style and cultural height of the product to increase distance from the audience or use short sentences. Bring the audience closer to the audience with a warm and caring tone. More and more consumers show the interest to luxury brands. The authors argue that when dealing with translations, one should try to reflect the characteristics of each module and convey that effect.

“Good” and “bad” adaptation to prison isolation of Polish long-term prisoners in the light of Erving Goffman’s typology of adaptation

The methods of adaptation to prison isolation of prisoners sentenced to short and long-term sentences differ significantly. The latter, after the stage of initial rebellion, often develop constructive coping strategies, while the former are able to persist in rebellion throughout their sentence, realizing its short duration. Based on his own field research regarding adaptation to prison isolation of Polish long-term prisoners, the author reflects upon which methods of adaptation (on the basis of Ervin Goffman’s typology) may be referred to as “good” and which as “bad” ones.

HUMOROUS SKILL OF THE WRITER MARHABAT BAIGUT

The article analyzes the problem of humor in the works of the writer Marhabat Baigut. The place and artistic function of humor in the ideological, thematic, author-worldview, figurative and stylistic terms in the writer's prose works are studied. The writer's unique style in the use of humor is seen as a creative achievement. The pathos that evokes the reader's amusement and easy smile comes from the character of the work, the behavior of the characters, the skills, the actions, the general pictorial components. Situations created by humor in depicting everyday life and reality, conflicts, relationships, types of language use are differentiated, and humor is recognized as one of the most rational techniques found in the writer's works. The active use of dialogue in the works under study in order to comprehensively cover, accurately and clearly convey what the writer wants to say, through the use of humor, language turns, short and unexpected sentences, a successful monologue, dialogue characterized by naturalness, as well as the author's narrative patterns, details are shown on concrete examples and confirmed by the concept in scientific works. It is also said about his good humor, which leads the reader to serious thoughts, light, sincere jokes in a friendly atmosphere perform an important social function. The goal was to identify the place of humor in the writer's work, which is deeply connected with the identity of the Kazakh people.

Analysis of the Integration of Poetry and Painting in William Carlos Williams' April

William Carlos Williams (William Carlos Williams 1883-1963) is one of the most famous American poets in the 20th century. His creation is characterized by the use of spoken language, trying to convey the feelings with concise and clear descriptive images and loose short sentences. The integration of poetry and painting is another major feature of his works, that is, integrating the painting art into the poetry creation, making the text full of light and color art, and making the whole poem more vivid and realistic. "April" is appropriate wording, using the combination of activity and inertia , to describe the images in the poem like a painting. It is a poem that visually reflects the spring, with philosophical and negative complex emotions inside. Based on Kandinsky's colorology and Williams's poetics theory, this paper explores the artistic effect of poetry and painting shown by this poem from the three aspects of color description, form and image.

Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives

In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information.

An automatic lip reading for short sentences using deep learning nets

One study whose importance has significantly grown in recent years is lip-reading, particularly with the widespread of using deep learning techniques. Lip reading is essential for speech recognition in noisy environments or for those with hearing impairments. It refers to recognizing spoken sentences using visual information acquired from lip movements. Also, the lip area, especially for males, suffers from several problems, such as the mouth area containing the mustache and beard, which may cover the lip area. This paper proposes an automatic lip-reading system to recognize and classify short English sentences spoken by speakers using deep learning networks. The input video extracts frames and each frame is passed to the Viola-Jones to detect the face area. Then 68 landmarks of the facial area are determined, and the landmarks from 48 to 68 represent the lip area extracted based on building a binary mask. Then, the contrast is enhanced to improve the quality of the lip image by applying contrast adjustment. Finally, sentences are classified using two deep learning models, the first is AlexNet, and the second is VGG-16 Net. The database consists of 39 participants (32 males and 7 females). Each participant repeats the short sentences five times. The outcomes demonstrate the accuracy rate of AlexNet is 90.00%, whereas the accuracy rate for VGG-16 Net is 82.34%. We concluded that AlexNet performs better for classifying short sentences than VGG-16 Net.