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16504 papers
Introduction: Effective communication often involves expressing disagreement while maintaining social harmony, which is influenced by cultural and linguistic norms. Native speakers of English typically employ various politeness strategies in their disagreement speech acts. However, Iraqi EFL learners may navigate these strategies differently due to variations in cultural norms and language proficiency. Therefore, the current study aimed to contrastively analyze the way Iraqi EFL learners and native English speakers perform the speech act of disagreement in light of politeness. Methodology: In this regard, a discourse completion test (DCT) was administered to 66 participants, comprising 33 Iraqi EFL students studying English as a foreign language (TEFL) and 33 native English speakers. The DCT was made up of scenarios that mirrored real-life circumstances in order to provoke responses from people who disagreed with them. Brown and Levinson’s (1987) theory of politeness was employed to analyze participants’ utterances. Results: The findings indicated that while expressing disagreement with people of higher, participants in both groups were more concerned with keeping their interlocutors’ positive faces. Furthermore, the study findings indicated that despite differences in the two groups of participants, Iraqi EFL learners utilized positive indirect politeness strategies more frequently than English native speakers. On the other hand, English native speakers applied direct and negative politeness strategies. Conclusion: Generally, the findings indicated that both groups tended to use the most direct type of disagreement as the social distance and power relation decreased.
Abstract This paper explores how to syntactically parse Ancient Greek texts automatically and maps ways of fruitfully employing the results of such an automated analysis. Special attention is given to documentary papyrus texts, a large diachronic corpus of non-literary Greek, which presents a unique set of challenges to tackle. By making use of the Stanford Graph-Based Neural Dependency Parser, we show that through careful curation of the parsing data and several manipulation strategies, it is possible to achieve an Labeled Attachment Score of about 0.85 for this corpus. We also explain how the data can be converted back to its original (Ancient Greek Dependency Treebanks) format. We describe the results of several tests we have carried out to improve parsing results, with special attention paid to the impact of the annotation format on parser achievements. In addition, we offer a detailed qualitative analysis of the remaining errors, including possible ways to solve them. Moreover, the paper gives an overview of the valorisation possibilities of an automatically annotated corpus of Ancient Greek texts in the fields of linguistics, language education and humanities studies in general. The concluding section critically analyses the remaining difficulties and outlines avenues to further improve the parsing quality and the ensuing practical applications.
Dialogue-level dependency parsing has received insufficient attention, especially for Chinese. To this end, we draw on ideas from syntactic dependency and rhetorical structure theory (RST), developing a high-quality human-annotated corpus, which contains 850 dialogues and 199,803 dependencies. Considering that such tasks suffer from high annotation costs, we investigate zero-shot and few-shot scenarios. Based on an existing syntactic treebank, we adopt a signal-based method to transform seen syntactic dependencies into unseen ones between elementary discourse units (EDUs), where the signals are detected by masked language modeling. Besides, we apply single-view and multi-view data selection to access reliable pseudo-labeled instances. Experimental results show the effectiveness of these baselines. Moreover, we discuss several crucial points about our dataset and approach.
BACKGROUND: Mood disorders are often associated with hypothalamic-pituitary-adrenal (HPA) axis dysfunction, and rumination has been implicated in delayed cortisol recovery. However, research findings on the impact of rumination on cortisol recovery have been inconsistent. The moderating effects of scalp prefrontal-limbic connections on the relationship between rumination and cortisol recovery may explain these discrepancies. METHOD: Acute stress was induced by a 5-min simulated job interview. Salivary samples and affective ratings were collected at seven pre-determined time points. After the simulated job interview, 35 healthy adult participants were randomly assigned to either the rumination condition (n = 17) or the distraction condition (n = 18). RESULTS: Inducing stress and rumination led to increased cortisol levels, negative mood, and state rumination. Compared with the distraction group, the rumination group displayed delayed cortisol recovery and decreased scalp prefrontal-limbic connectivities, that is, left ventrolateral prefrontal cortex (LVLPFC) and left temporal area (LTMP) [ps <.05], and right dorsolateral prefrontal cortex (RDLPFC) and anterior cingulate cortex (ACC) [ps <.05]. The relationship between rumination and cortisol recovery was moderated by connectivities between the left dorsolateral prefrontal cortex (LDLPFC) and LTMP, RDLPFC and LTMP, LDLPFC and ACC, and RDLPFC and ACC [B = -0.98 to -0.35, SE = 0.15-0.34, ps <.05]. Higher rumination combined with reduced scalp prefrontal-limbic connectivities to predict delayed cortisol recovery. CONCLUSION: The current findings suggest that scalp prefrontal-limbic connectivity is a neural underpinning related to emotion regulation for the effects of state rumination on stress recovery. These findings also provide a potential target for non-invasive intervention in HPA axis dysregulation.
This paper introduces CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, designed to capture continuous affect data about interpersonal perceptions in dyadic interactions. Grounded in behavioral ecology perspectives of emotion, this approach replaces valence as the relevant rating dimension with approach and withdrawal, reflecting the degree to which behavior is perceived as increasing or decreasing social distance. We conducted a study to experimentally validate the efficacy of our platform with 24 participants. The tool’s effectiveness was tested in the context of dyadic negotiation, revealing insights about how interpersonal dynamics evolve over time. We find that the continuous affect rating method is consistent with individuals’ perception of the overall interaction. This paper contributes to the growing body of research on affective computing and offers a valuable tool for researchers interested in investigating the temporal dynamics of affect and emotion in social interactions.
This research paper aims to present the feminist side of translation. It aims to reconsider and reshape translation from the feminist point of view. It does so by introducing the feminist theory to translation and applying it to some excerpts. These excerpts are taken from the great English novelist Virginia Woolf’s (1977) A Room of one’s own. The theory in this study is the feminist theory in translation by the famous theorist Louise von Flotow (1991). This theory contains three strategies. The first is supplementing. It means adding elements to the translation to compensate for what the language lacks, such as gender agreement in English. This strategy fills the gap between conventional linguistic norms and feminist purposes. The second strategy is Footnotes and prefaces which present explanations for translational choices and linguistic references at the beginning of the text or throughout it to make women’s voices visible. The third strategy is Hijacking which is an appropriation of the text, including the changes applied to it to suit feminist translators’ aims.
Moral decision-making is influenced by various factors, including personality and language. In this cross-sectional study, we investigated the Foreign-Language effect (FLe) in early, highly proficient, Catalan-Spanish bilinguals and examined the role of several personality dimensions in their responses to moral dilemmas. We obtained a multilevel data structure with 766 valid trials from 52 Catalan-dominant undergraduate students who read and responded anonymously to a computerized task with 16 standardized moral dilemmas, half in Catalan and half in Spanish. Results of a multilevel multivariate logistic regression analysis showed that consistent with previous research, participants gave more utilitarian responses to impersonal than personal dilemmas. The language of the dilemma had no effect on the response (dichotomous: utilitarian vs. deontological), decision time, or affective ratings, contradicting the hypothesis of shallower emotional processing of the information in the second language. Interestingly, cruelty features of psychopathy were significantly associated with an enhanced proportion of utilitarian decisions irrespective of the language or the nature of the dilemmas. Furthermore, cruelty features interacted with participants' assessment of dilemma aspects like vividness and verisimilitude. Overall, our findings suggest that early bilinguals immersed in a dual-language context using close Romance languages do not show the FLe and that personality traits like cruelty can modulate moral decisions regardless of language or dilemma type.
It is widely known that English teaching practices are largely rooted in colonialism and linguistic imperialism – the belief in the superiority of the Western teaching methods and linguistic norms established in such countries as the UK and the US. The current trend to decolonize English teaching has been gaining momentum for over three decades but has not entirely penetrated into the mainstream teaching of English, with English for Academic Purposes not being an exception. Relatively little focus has been placed in the research onto non-English speaking contexts with the purpose of analysing the current state of EAP provision at universities and gauging the influence of the native-speakerist academic English norms on EAP students’ writing. In the case of the EU context, it may be particularly pertinent to investigate the potential influence that Brexit might have had in terms of rejecting the imposition of the Standard British English norms and associated teaching approaches. This paper is meant to be a reflection with an attempt to stimulate the discussion of EAP teaching practices and academic discourses in the EU higher education in the post-Brexit era. It will consider the issues in the EAP provision in the EU with the example of Portuguese HE and will reflect on the native-speakerist tendencies within the academia and ways to tackle the dominance of the Anglophone norms. This paper hopes to contribute to the argument in favor of the decolonization of EAP teaching practices in non-English speaking contexts, as decolonization can help foster a more equitable and inclusive world.
Objective: Insomnia affects 30-45% of the world population, is related to mortality (i.e., auto accidents and job-related accidents), and is related to mood and affect disorders such as anxiety and depression. Better understanding of insomnia via increased research will decrease the burden on insomnia. The neurocognitive model of sleep proposes that conditioned somatic and cognitive hyperarousal develop in response to repeated pairings of sleep-related stimuli with insomnia-related wakefulness. The purpose of this study was to examine the neurocognitive model of sleep using a novel laboratory paradigm, the Sleep Approach Avoidance Task (SAAT). It was hypothesized that individuals who report symptoms of insomnia will display a bias for negative sleep-related images from the SAAT, which is presumably a reflection of cognitive, behavioral and physiological processes associated with hyperarousal. It was also hypothesized that participants who report poor sleep would provide different subjective ratings for negative images (i.e., stronger valence and arousal) than individuals who reported better sleep. Participants and Methods: An initial sample of 66 healthy college-aged participants completed the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI) the Dysfunctional Attitudes and Beliefs about Sleep (DBAS) scale and the Epworth Sleepiness Scale (ESS). Participants also completed the SAAT. The SAAT was developed to assess sleep-related bias in adults. The SAAT is a visual, joystick controlled reaction time task that measures implicit bias for positive and negative sleep-related images. At the end of the task the participants are also asked to rate each image along three dimensions included valence, arousal and dominance. Results: There was a positive correlation between the SAAT and the ISI [r(61) =.30, p =.01], indicating that symptoms of insomnia are related to negative approach-related bias for sleep-related images. No other correlations were observed between the SAAT and self-report sleep measures. With regard to rating of images, higher dominance ratings for negative images were correlated with the SAAT [r(62) =.24, p =.03], which indicates that the approach bias for negative images is associated with “being in control.” Multiple linear regression was used to test if ISI scores and dominance ratings for negative images significantly predicted SAAT bias scores. The overall regression was statistically significant [r2 =.13, F(2, 58) = 4.15, p =.02]. ISI scores significantly predicted SAAT scores (ß =.27, p =.04), whereas dominance ratings for negative images did not significantly predict SAAT scores (ß =.20, p =.11). Exploratory correlational analyses were also completed for ratings of images and other sleep self-report measures. Valence ratings for positive sleep-related images were positively correlated with the ESS [r(64) =.36, p =.01], whereas valence ratings for negative sleep-related images were negatively correlated with the ESS [r(64) = -.24, p =.03]. Conclusions: Hypotheses were partially supported with the ISI being the only self-report measure associated with negative bias for sleep-related images. While ratings of dominance are associated with bias for negative sleep-related images, these ratings do not provide unique variance. These findings indicate a cognitive processing bias for sleep-related stimuli among young adult poor sleepers. Limitations, implications for assessment and intervention are discussed.
GUARDIAN-MT (GUarded by Advanced Radar technology-based Diagnostics Applied in palliative and intensive care Nursing – Music Therapy) was the music therapy subproject within a research examining the reliability of non-contact vital parameter measurements using radar, compared to validated measurement methods. The focus of GUARDIAN-MT was on evaluating live-played sounds during a cold-pressor induced pain stimulus using a mixed-method approach. In addition to direct standard measures, a content-analytical procedure was applied to analyze telephone interviews regarding the retrospective experience of the pain event according to psychologically relevant experiential categories. It was found that particularly the arousal ratings, assessed by independent evaluators, had a substantial influence on the perceived valence of the pain event. Arousal-reducing interventions, such as well-coordinated performance of individual live music, can contribute to experiencing the pain not only as milder, but also less prolonged. The analysis of qualitative data can help uncover and deepen the essential qualities and effects of music therapy interventions.
Mornings are salient times for disrupted affect that may be impacted by prior sleep. The current study extends work linking sleep disruptions with negative affect by examining how nightly changes in sleep duration, timing, and quality relative to a person's average impact morning affect. We further tested whether depression severity moderated the relationship between nightly variations in sleep and morning affect. This is a secondary analysis of participants ages 18-65 years with varying levels of depression (N = 91) who wore an Actiwatch for 3-17 days (n = 73) while reporting morning affect using a visual analogue scale. Multilevel models tested the previous night's sleep duration, timing, or quality as a predictor of morning affect. Sleep measures were group-mean centred to account for nightly variation in participants' sleep. A cross-level interaction between depression severity and nightly sleep was entered. Sleeping longer (b = 0.1; p < 0.001) and later (b = 1.8; p = 0.01) than usual were both associated with better morning mood. There was a significant interaction between nightly actigraphic sleep duration and depression severity on morning affect (b = 0.003; p = 0.003). Participants with higher depression severity reported worse affect upon waking after sleeping less than their usual. In comparison, sleeping less than usual did not affect morning affect ratings for participants with lower depression. A similar interaction was found for sleep quality (b = 0.02; p < 0.001). There was no interaction for midsleep timing. Sleeping less than usual impacted morning affect in individuals with greater depression, potentially suggesting a pathway by which sleep disturbances perpetuate depression.
Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs’ powerful ability in learning expressive graph representations. However, most of these works learn graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone, and the errors introduced in the static graph cannot be corrected and might accumulate in later stages, and (2) the graph construction stage and graph representation learning stage are disjoined, which negatively affects the model’s running speed. In this paper, we propose a joint-learning-based dynamic graph learning framework and apply it to two typical structured prediction tasks: syntactic dependency parsing, which aims to predict a labeled tree, and semantic dependency parsing, which aims to predict a labeled graph, for jointly learning the graph structure and graph representations. Experiments are conducted on four datasets: the Universal Dependencies 2.2, the Chinese Treebank 5.1, the English Penn Treebank 3.0 in 13 languages for syntactic dependency parsing, and the SemEval-2015 Task 18 dataset in three languages for semantic dependency parsing. The experimental results show that our best-performing model achieves a new state-of-the-art performance on most language sets of syntactic dependency and semantic dependency parsing. In addition, our model also has an advantage in running speed over the static graph-based learning model. The outstanding performance demonstrates the effectiveness of the proposed framework in structured prediction.
Social touch is crucial for human well-being, as a lack of tactile interactions increases anxiety, loneliness and need for social support. To address the detrimental effects of social isolation, we build on cutting-edge research on social touch and movement sonification to investigate whether social tactile gestures could be perceived through sounds, a sensory channel giving access to remote information. Four experiments investigated participants’ perception of auditory stimuli that were recorded with our ‘audio-touch’ sonification technique, which captures the sounds of touch. In the first experiment, participants correctly categorized sonified skin-on-skin tactile gestures (i.e., stroking, rubbing, tapping, hitting). In the second experiment, the audio-touch sample consisted of the sonification of six socio-emotional intentions conveyed through touch (i.e., anger, attention, fear, joy, love, sympathy). Participants’ valence ratings of the underlying touch were coherent with the intended emotions, while their categorization presented more variability. In two additional experiments investigating the characteristics of the surface involved in the tactile interactions (i.e skin or object), skin proved to be a critical factor in the auditory categorization of both gestural and emotional audio-touch stimuli. This result reveals that human skin-on-skin interactions convey information which sets them apart, perceptually, from object-on-object interactions. Our research thus unveils that social touch can be perceived through sounds, when they are obtained with our specific sonifying methodology, tailored for skin-on-skin interactions. This bears great promise for giving remote access, through the auditory channel, to meaningful social touch interactions.
Syntax is a part of linguistics that studies how words are combined into sentences and phrases. In this study, we focus on analyzing the syntax of noun phrases in Indonesian. We use the treebank analysis technique to identify the syntactic structure of noun phrases in Indonesian. Study results show that noun phrases in Indonesian have complex and varied syntactic structures and that the context and types of noun phrases play an important role in influencing the syntactic structure of noun phrases. These findings are important for understanding how noun phrases are formed and understood in Indonesian and how it affects language processing.
This corpus-based study aimed to investigate the presence of context-dependent linguistic errors in a corpus of clinical reports. The data were taken from a corpus comprising more than 2 million words and made up of clinical reports from emergency medicine, intensive care unit, general surgery, and psychiatry. Quantitative and qualitative analyses were carried out. A language model based on n-grams was developed for the detection of errors, parameters for the selection of cases were defined, and a classification tool was implemented. The findings indicated that emergency medicine was the medical specialty with the highest number of context-dependent errors and that the most frequent type of error was omission of written accent. Furthermore, the analysis revealed the presence of errors of competence due to the incorrect application of the linguistic norm of Spanish, phenomena of phonetic similarity, and composition of words; it is also worth noting that performance errors occurred due to rapid typing on the keyboard. This study constituted the first analysis and creation of a typology of context-dependent errors for the medical domain in Spanish. It contributed to the design of a module based on linguistic knowledge that can be used for the development and improvement of automatic correction systems that, in turn, are used for data processing in medicine.
The article is devoted to the study of the texts of the museum exhibition, which play the mediator role between visitors of a museum and its artifacts. The purpose of the study is to consider the text of the museum exhibition as an effective tool for intercultural communication. The review of foreign and domestic works shows that multilingual museum exposition helps to develop a dialogue and understanding between different cultures. The author studied the museum labels of the State Hermitage Museum, their grammatical and stylistic peculiarities, which should be taken into account while translating museum texts from Russian into English. It is determined that the grammatical peculiarities of the museum texts are verbless constructions, incomplete sentences, where linguistic norms are violated. The style of the texts of museum expositions is, as a rule, publicistic, some texts also have features of scientific, spoken and literary styles. While translating, the translator should aim to save the meaning and functional style.
BACKGROUND AND OBJECTIVES: Previous research identified cognitive reappraisal as an adaptive emotion regulation strategy. However, theories on emotion regulation flexibility suggest that reappraisal effectiveness (RE) may depend on an individual's familiarity with stressors. In this study, we expect high reappraisal inventiveness (RI), i.e., the generation of many and categorically different reappraisals, to increase RE for individuals with low situational familiarity. Individuals with high situational familiarity, however, would be more effective with low RI. DESIGN: A total of 148 participants completed the Script-based Reappraisal Task, in which they were presented with fear- and anger-eliciting scripts. Depending on trial type, participants were instructed to reappraise (reappraisal-trial) or react naturally (control-trial) to the scripts. After each trial, participants indicated affective states and reappraisals. We assessed RI and calculated RE-scores as difference between affect ratings in reappraisal- and control-trials for valence and arousal. Finally, participants rated the familiarity with each situation. RESULTS: The results indicated a significant moderating effect of situational familiarity on the relationship between RI and RE-valence (not RE-arousal). The moderation was mainly driven by a detrimental effect of RI for individuals with high situational familiarity. CONCLUSIONS: Our results hint at the importance of individual experience with emotional content in the research of cognitive reappraisal.
The modern linguistic studies confirm that keeping pace with modern linguistic norms requires careful usage of terminology in order to be properly employed through which the translation process is promoted and the problem of translating the specialized terminology in general and particularly economic terms are among the problematic encountered in transferring knowledge from foreign languages to Uzbek language. The purpose of this study is to analyze these problems in order to propose solutions for the unification of the term in English, refuting the reasons for the unification of the English terminology, because the terms are the keys to science, so if there are multiple terms equivalent to a single one, this leads to a disturbance in understanding and reflects negatively on the assimilation of knowledge, and contributes to the confusion of the entire translation process.
Genre classification for text documents is useful in media monitoring and detection of misinformation. Recent work in text classification for genre has shown that advanced algorithms such as neural networks and transformers are well-suited for the purpose. However, for shorter text documents, such as those obtained from social media or news articles, training of deep learning models becomes challenging since they require a large amount of input. Furthermore, genre classification of text summaries, such as headlines of news, is an important direction which has not been explored at large. In this work, the effect of Extractive and Abstractive Summarization on classification for genre of text documents was evaluated. Gensim summarizer was used to obtain extractive summaries and the Pegasus summarizer to obtain abstractive summaries. For classification, two classes of genres, Fiction and Non-fiction, were considered while the gold standard Brown Corpus was used for experimentation. The features used for genre classification were frequencies of various Part-of-Speech (PoS) tags derived from five Penn TreeBank annotated tags. Logistic Regression (LR) and Support Vector Machines (SVM) were used for classification purposes. The results of classification were better for summaries obtained using the extractive technique, indicating that the features of extractive summaries remain in agreement with the documents from which the summary is constructed as compared to abstractive summaries. Further, the SVM classifier performed better than the LR classifier. For exhaustive coverage of the research goal, further experimentation with the number of words of the output summaries of the extractive technique was performed to arrive at a threshold value of the length of summaries. The value indicated that summaries as short as 80 words can be successfully classified using this method.
<h3>BACKGROUND AND PURPOSE:</h3> Brain atrophy is an important surrogate for brain reserve, the capacity of the brain to cope with acquired injuries such as acute stroke. It is unclear how well atrophy measurements on MR imaging can be reproduced using NCCT imaging. We aimed to compare pragmatic atrophy measures on NCCT with MR imaging in patients with acute ischemic stroke. <h3>MATERIALS AND METHODS:</h3> This is a post hoc analysis, including baseline NCCT and 24-hour follow-up MR imaging data from the Safety and Efficacy of Nerinetide (NA-1) in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial. Cortical atrophy was measured using the global cortical atrophy scale, and subcortical atrophy was measured using the intercaudate distance-to-inner-table width (CC/IT) ratio. Agreement and correlation between these measures on NCCT and MR imaging were calculated using the Gwet agreement coefficient 1 and Pearson correlation coefficients, respectively. <h3>RESULTS:</h3> Among 1105 participants in the ESCAPE-NA1 trial, interpretable NCCT and 24-hour MR imaging were available in 558 (50.5%) patients (mean age, 67.2 [SD, 13.7] years; 282 women). Cortical atrophy assessments performed on NCCT underestimated atrophy severity compared with MR imaging (eg, patients with global cortical atrophy of ≥1 assessed on NCCT = 133/558 [23.8%] and on MR imaging = 247/558 [44.3%]; a 20.5% difference). Overall, cortical (ie, global cortical atrophy) atrophy assessments on NCCT had substantial or better agreement with MR imaging (Gwet agreement coefficient 1 of > 0.784; <i>P </i><.001). Subcortical atrophy measures (CC/IT ratio) showed strong correlations between NCCT and MR imaging (Pearson correlation = 0.746, <i>P </i><.001). <h3>CONCLUSIONS:</h3> Brain atrophy can be evaluated using simple measures in emergently acquired NCCT. Subcortical atrophy assessments on NCCT show strong correlations with MR imaging. Although cortical atrophy assessments on NCCT are strongly correlated with MR imaging ratings, there is a general underestimation of atrophy severity on NCCT.
Abstract The present study analyzes the transformation of the vowel system and especially the process of vowel mergers based on the Latin inscriptions of the Gallic and Germanic provinces. With the help of the Computerized Historical Linguistic Database of the Latin Inscriptions of the Imperial Age ( http://lldb.elte.hu/ ), it tries to draw and then compare the phonological profiles of the selected provinces and to describe the dialectal position of Gaul and the Germanic provinces regarding vocalism in three periods (AD 1–300, 301–500 and 501–700). The analysis, which also covers comparisons with certain provinces of Italy, Spain and Dalmatia, is carried out considering four aspects: the ratio of vocalic versus consonantal changes, the ratio of vowel mergers compared to vocalic changes, the ratio of e-i and o-u mergers compared to each other, and the ratio of vowel mergers by stressed and unstressed syllable. As a result of the present study, it was revealed that Gallic provinces cannot be treated as a unit or as clearly separate from the other areas studied according to either aspect of the study, especially not in the early, pre-Christian period. Gallic provinces appear to behave in the same or a levelled manner at most in the later and/or latest periods. The Germanic provinces, especially Germania Superior, have, albeit with some delay, adapted to the Gallic provinces in their late development. The present study, which continued József Herman's research, managed to explore the hitherto little-known linguistic and dialectological features of Latin in the Gallic and Germanic provinces.
In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks. Our approach uses a custom dictionary of action words, designed to store together words that share meaning, allowing for easy expansion of the vocabulary by adding more action words from a lexical database. We validate our Natural Language Robot Programming (NLRP) framework through simulation and real-world experimentation, using a Franka Panda robotic arm equipped with a calibrated camera-in-hand and a microphone. Participants were asked to complete a pick-and-place task using verbal commands, which were converted into text using Google's Speech-to-Text API and processed through the NLRP framework to obtain joint space trajectories for the robot. Our results indicate that our approach has a high system usability score. The framework's dictionary can be easily extended without relying on transfer learning or large data sets. In the future, we plan to compare the presented framework with different approaches of human-assisted pick-and-place tasks via a comprehensive user study.
Hoarding disorder is characterised by the acquisition of, and failure to discard large numbers of items regardless of their actual value, a perceived need to save the items and distress associated with discarding them, significant clutter in living spaces that render the activities associated with those spaces very difficult causing significant distress or impairment in functioning. To aid development of an intervention for hoarding disorder we aimed to identify current practice by investigating key stakeholders existing practice regarding identification, assessment and intervention associated with people with hoarding disorder. Two focus groups with a purposive sample of 17 (eight male, nine female) stakeholders representing a range of services from housing, health, and social care were audio recorded, transcribed verbatim and analysed thematically. There was a lack of consensus regarding how hoarding disorder was understood and of the number of cases of hoarding disorder however all stakeholders agreed hoarding disorder appeared to be increasing. The clutter image rating scale was most used to identify people who needed help for hoarding disorder, in addition to other assessments relevant to the stakeholder. People with hoarding disorder were commonly identified in social housing where regular access to property was required. Stakeholders reported that symptoms of hoarding disorder were often tackled by enforced cleaning, eviction, or other legal action however these approaches were extremely traumatic for the person with hoarding disorder and failed to address the root cause of the disorder. While stakeholders reported there was no established services or treatment pathways specifically for people with hoarding disorder, stakeholders were unanimous in their support for a multi-agency approach. The absence of an established multiagency service that would offer an appropriate and effective pathway when working with a hoarding disorder presentation led stakeholders to work together to suggest a psychology led multiagency model for people who present with hoarding disorder. There is currently a need to examine the acceptability of such a model.
Vision-Language Pre-training (VLP) has advanced the performance of many visionlanguage tasks, such as image-text retrieval, visual entailment, and visual reasoning. The pre-training mostly utilizes lexical databases and image queries in English. Previous work has demonstrated that the pre-training in English does not transfer well to other languages in a zero-shot setting. However, multilingual pre-trained language models (MPLM) have excelled at a variety of single-modal language tasks. In this paper, we propose a simple yet efficient approach to adapt VLP to unseen languages using MPLM. We utilize a cross-lingual contextualized token embeddings alignment approach to train text encoders for non-English languages. Our approach does not require image input and primarily uses machine translation, eliminating the need for target language data. Our evaluation across three distinct tasks (image-text retrieval, visual entailment, and natural language visual reasoning) demonstrates that this approach outperforms the state-of-the-art multilingual vision-language models without requiring large parallel corpora. Our code is available at https://github.com/Yasminekaroui/CliCoTea.
The Japanese CCGBank serves as training and evaluation data for developing Japanese CCG parsers. However, since it is automatically generated from the Kyoto Corpus, a dependency treebank, its linguistic validity still needs to be sufficiently verified. In this paper, we focus on the analysis of passive/causative constructions in the Japanese CCGBank and show that, together with the compositional semantics of ccg2lambda, a semantic parsing system, it yields empirically wrong predictions for the nested construction of passives and causatives.
Tastes affect the body and our emotions. We used tasteless, sweet, and bitter stimuli to induce participants' moods, and we examined the effect of mood on an emotional evaluation of pleasant, neutral, and unpleasant images using event-related potentials, N2, N400, and late positive potential (LPP), which reflect emotional evaluation in the brain. The results indicated that mood valence was most positive for sweetness and most negative for bitterness. Moreover, there was no significant mood effect on subjective valence ratings of emotional images. Furthermore, the N2 amplitude, which is related to the early semantic processing of preceding stimuli, was unaffected by the taste induced mood. In contrast, we found that the N400 amplitude, which is related to the mismatch of emotional valence between stimuli, increased significantly for unpleasant images when participants were in a positive rather than negative mood state. Also, the LPP amplitude, which is related to the emotional valence of images, showed only the main effect of the images' emotional valence. The N2's results suggest that the early semantic processing of taste stimuli might have had a negligible impact on emotional evaluation because taste stimuli minimize semantic processing that accompanies mood induction. In contrast, the N400 reflected the effects of the induced mood, and the LPP reflected the impact of the valence of emotional images. The use of taste stimuli to induce mood revealed different brain processing of taste-induced mood effects on emotional evaluation, including N2's involvement in semantic processing, N400's involvement in matching emotions between mood and stimuli, and LPP's involvement in subjective evaluations of stimuli.
Aesthetic evaluations, including beauty and attractiveness, have an important role in our lives. Despite its importance in our every-day life, enough attention has not been devoted to the assessment of place attractiveness in previous studies. We assume that changes in elements of square attractiveness are associated with changes in brain functional connectivity patterns. In this study, we have tried to explore the relationship between elements of square attractiveness and individuals' emotional perception as well as the brain mechanism involved in the process of cognitive development. There has been a focus on using objective measures of physiological rather than using self-reported data of an individual's emotions because people cannot understand their emotions properly and it is needed to compare self-report emotions with physiological processes. Classification of the five main elements of attractiveness was performed using the Delphi technique. Subsequently, twenty-four healthy young adults were exposed to the visual stimuli consists of five elements. A 32-channel EEG system was used to record the brain activities of participants while watching the stimuli. The subjects' feelings about valence and arousal levels of the elements were evaluated using the Self-Assessment Manikin (SAM) technique. The findings showed that “visual openness” is the most important element to increase the square attractiveness of everyday landscape in residential areas. The analysis revealed a significant difference (p = 0.048) in arousal ratings between more attractive (more openness) (M = 4.77) and less attractive (less openness) (M = 4.52). Attractiveness elements of the stimuli have a region-specific association with brain functional connectivity networks. This pattern is mainly found in the functional connections between central parts of the brain.
Real-world applications of language models entail data privacy constraints when learning from diverse data domains. Federated learning with pretrained language models for language tasks has been gaining attention lately but there are definite confounders that warrants a careful study. Specifically, understanding the limits of federated NLP applications through varying the effects of different aspects (such as data heterogeneity, the trade-off between training time and performance, the effect of different data, and client distributions and sensitivity of the shared model to learning local distributions) is necessary to evaluate whether language models indeed learn to generalize by adapting to the different domains. Towards that, we elaborate different hypotheses over the components in federated NLP architectures and study them in detail with relevant experiments over three tasks: Stanford Sentiment Treebank-2, OntoNotes-5.0 and GigaWord. The experiments with different Transformer inductive biases on the variety of tasks provide a glimpse at the understanding of federated learning at NLP tasks. Specifically, the analysis suggests that regularization due to the ensembling effect may be masquerading as domain adaptation of federated learning in NLP with pre-trained language models.
Genres such as indie (Beal 2009) and hip-hop (Eberhardt & Freeman 2015) feature dialectal traits in English, but whether genres form targets distinct from speech remains unclear. We examine genre effects on phonetic variation in Quebec French music by probing the role of genres (pop, country, alternative, and indie) on laxing and diphthongization, processes characteristic of Quebec French (Walker 1984). Stigma facing formal varieties of Quebec French has vanished within dialect (Kircher 2012), yet remains for processes that vary regionally or socioeconomically (Côté 2012; Côté & Lancien, 2019). Whereas laxing is categorical and non-stigmatized (Côté 2012; Paradis & Dolbec, 1998), diphthongization is variable and stigmatized (Côté 2012). We use a novel corpus of ten Québécois singers who released multiple albums from 2011-2021 (29 albums; 326 songs). We find the emergence of genre-specific linguistic norms distinct from speech and argue that genres in music parallel sociolects.
Charles Dickens (1812-1870) achieved a recognizableplace among English writers through the use of the stylisticfeatures in his fictional language. This study is concernedwith Dickens' unique fictional language, used in one of hisnovels entitled "Hard Times", in relation to phonologicaldeviation from settled norms in English. It endeavors toshow Dickens' manipulating language and the effectsachieved through this manipulation.This research investigates Dickens' use of languagewhich deviates from the linguistic norm phonologically. Assuch, it is hypothesized that Dickens used phonologicaldeviation to show the character's social class.The study aims to analyze the types of phonologicaldeviations in Dickens' "Hard Times". It determines thereasons behind these deviations, and how that reflects
Abstract We describe the first steps in preparation of a treebank of 14 th -century Czech in the framework of Universal Dependencies. The Dresden and Olomouc versions of the Gospel of Matthew have been selected for this pilot study, which also involves modification of the annotation guidelines for phenomena that occur in Old Czech but not in Modern Czech. We describe some of these modifications in the paper. In addition, we provide some interesting observations about applicability of a Modern Czech parser to the Old Czech data.
The author argues that modern digital etiquette is in the process of formation and constantly evolving. Old norms of business correspondence do not always align with the current conditions and users’ views on effective and ethical communication. Researchers are faced with the question of what can serve as a source of information on emerging ethical and linguistic norms. The purpose of the article is to prove that the analysis of language reflection can be an effective way to study the norms of modern digital etiquette. The study conducted in the fall of 2022 used the method of analyzing the corpus of language reflection. The dataset obtained through a two-stage survey includes an assessment by 3464 Internet users of 92 formulas and expressions used in digital correspondence. The article analyzes the state of modern digital etiquette, substantiates the need for the analysis of language reflection to study ethical and speech norms, describes the methodology of studying language reflection through surveys, and con cludes on the necessity of analyzing language reflection to capture trends in digital etiquette. The novelty of the article is determined by at least two factors. Firstly, it addresses the topic of unexplored and unstable norms of digital etiquette. Secondly, it highlights the need to analyze language reflection on the rules of speech behavior on the web. The significance of the study is related to the fact that digital etiquette norms are constantly changing, and researchers need a tool to capture such changes. The practical value of the study is that it enables certain recommendations on email etiquette and warns against potential communicative failures in internet communication.
Research into cultural tastes has commonly sought to analyze and understand preferences in terms of notions of familiarity. Such approaches are inadequate, however, when it comes to examining our engagement with unfamiliar cultural content. This paper responds to this gap by examining how people respond to algorithmic recommendations of culture through a case study of unfamiliar Australian art music. It firstly identifies three different “techniques’ by which audiences engage with and value music: functional, emotional, and intellectual. The analysis then examines how these techniques, together with measures of familiarity and the acoustic “materiality” of the music itself, combine to predict the affective ratings given to music recommendations. The findings show that audiences display a surprising capacity to engage with the unfamiliar. The paper argues for the need to develop more nuanced understandings of the relationship between familiarity and preferences which are capable of accommodating a taste for the unfamiliar.
This study aims to evaluate and provide recommendations by testing the usability level of the Odoo Operation & Maintenance ERP Module application. Using the SUS Questionnaire method, the results of usability testing were analyzed into each parameter of the SUS Questionnaire. The SUS questionnaire has an assessment criteria of “Unacceptable category” with a value range of 0–50 class F and has an adjective rating of “Worst Imaginable to Poor”, the category “Low Marginal” with a score range of 51-62.5 class F and has a word rating OK trait, Marginal High Category with a range of 62.6–70 grade D and adjective rating OK, and Acceptable Category with a score range of 70–100 grade C to A and adjective rating good, very good to best. The test results get a value of 53.25 including the OK Marginal Low category, meaning that it needs improvement so that the system can be well received by users. Further interviews were also conducted with ten informants to obtain a specific description of the complaints of system users. From the results of the interview, an analysis was carried out to provide evaluations and recommendations for improvement of the Odoo ERP Operation & Maintenance Module.
This article proposes to look at the theory of translanguaging from the angle of its universality/non-universality, bringing into discussion a new empirical case - speech strategies and practices of highly educated multilingual migrants from the former USSR and multilingual residents of modern Russia. The authors conclude that people whose socialization took place in this region are characterized by rigid ideas about the language norm and authenticity, about the “purity” of languages and high standards of speaking them. These concepts apply to all languages, both native and foreign. Traditional and conservative language ideologies are widespread in the Soviet and post-Soviet area, which is the reason why translanguaging is often perceived as careless speech, incomplete language competence. In this sense, this regional case demonstrates the non-universality of translanguaging theory and the importance of contexts in which attitudes towards multilingualism, linguistic norm and related linguistic phenomena are formed. It is primarily the western democratic, postcolonial context of translanguaging practice and theory that explains why the language attitudes and speech behavior of residents of (post)Soviet region do not fit into it.
OBJECTIVES: We used three-dimensional (3D) virtual images to undertake a subjective evaluation of how different factors affect the perception of facial asymmetry among orthodontists and laypersons with the aim of providing a quantitative reference for clinics. MATERIALS AND METHODS: A 3D virtual symmetrical facial image was acquired using FaceGen Modeller software. The left chin, mandible, lip and cheek of the virtual face were simulated in the horizontal (interior/exterior), vertical (up/down), or sagittal (forward or backward) direction in 3, 5, and 7 mm respectively with Maya software to increase asymmetry for the further subjective evaluation. A pilot study was performed among ten volunteers and 30 subjects of each group were expected to be included based on 80% sensitivity in this study. The sample size was increased by 60% to exclude incomplete and unqualified questionnaires. Eventually, a total of 48 orthodontists and 40 laypersons evaluated these images with a 10-point visual analog scale (VAS). The images were presented in random order. Each image would stop for 30 s for observers with a two-second interval between images. Asymmetry ratings and recognition accuracy for asymmetric virtual faces were analyzed to explore how different factors affect the subjective evaluation of facial asymmetry. Multivariate linear regression and multivariate logistic regression models were used for statistical data analysis. RESULTS: Orthodontists were found to be more critical of asymmetry than laypersons. Our results showed that observers progressively decreased ratings by 1.219 on the VAS scale and increased recognition rates by 2.301-fold as the degree of asymmetry increased by 2 mm; asymmetry in the sagittal direction was the least noticeable compared with the horizontal and vertical directions; and chin asymmetry turned out to be the most sensitive part among the four parts we simulated. Mandible asymmetry was easily confused with cheek asymmetry in the horizontal direction. CONCLUSIONS: The degree, types and parts of asymmetry can affect ratings for facial deformity as well as the accuracy rate of identifying the asymmetrical part. Although orthodontists have higher accuracy in diagnosing asymmetrical faces than laypersons, they fail to correctly distinguish some specific asymmetrical areas.
This paper offers the basic guidelines of a formalized version of the Lexical Constructional Model (LCM; Ruiz de Mendoza & Mairal Usón, 2008, 2011; Ruiz de Mendoza & Galera, 2014), the Formalized Lexical-Constructional Grammar (FL_CxG), which will pave the way for future computational developments, such as parsers or lexical databases. The FL_CxG deploys (i) the typologically oriented syntactic apparatus of Role and Reference Grammar (Van Valin, 2005; Van Valin & LaPolla, 1997), (ii) the catalogue of constructional units arranged in a 4-layer typology, as proposed by the LCM, and (iii) some insights for semantic representations from the Generative Lexicon Theory (Pustejovsky, 1995; Pustejovsky & Batiukova, 2019), and Minimal English (Goddard, 2018). All the components of the FL_CxG (lexical units and construct(ion)s) are formally encoded as Typed Feature Structures in the format of Attribute Value Matrixes. These units are to be understood as constraints operating in the unification processes which underlie the generation/decoding of a given fragment of language.
This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The result is a set of general models for Latin with good performance on a number of natural language processing tasks (e.g. the top-performing model yields POS tagging, 97.41% accuracy; lemmatization, 94.66% accuracy; morphological tagging 92.76% accuracy). The paper describes the model training, including its training data and parameterization, and presents the advantages to Latin-language researchers of having a spaCy model available for NLP work.
This work investigates the effect of grid-connected converter topology on equivalent converter output impedance with a specific focus on the diagonal dominance of the impedance matrix across a frequency range. When considering multiple-input multiple-output systems most traditional stability techniques are reliant on the diagonal dominance of the studied system. Therefore, a rating of diagonal dominance is proposed based upon the correlation coefficient between row and column in the impedance matrix. This provides a scale that ranges from off-diagonally dominant (-1) through uniformly distributed (0) and up to diagonally dominant (1) across a range of frequencies. The scale is used to specify which control structures can be considered as diagonally dominant at certain frequencies and which control components have the greatest effect on the rating. A direct relation is found between system exhibiting a diagonal dominance rating of 0.7 and above and the efficacy of traditional stability margins. Traditionally strong systems with low network impedance where controllers can be tuned conservatively exhibit high degrees of diagonal dominance and can be analysed quickly with traditional margins with minimal error. For systems exhibiting a lower rating, disk margins are explored as an alternative which offer greater accuracy. Additionally, more realistic perturbations of gain and phase occurring simultaneously in multiple channels can be considered which is more applicable for the modern electricity network with a high penetration of grid-connected converters.
Fine-tuning is a prominent technique to adapt a pre-trained language model to downstream scenarios. In parameter-efficient fine-tuning, only a small subset of modules are trained over the downstream datasets, while leaving the rest of the pre-trained model frozen to save computation resources. In recent years, a popular productization form arises as Model-as-a-Service (MaaS), in which vendors provide abundant pre-trained language models, server resources and core functions, and customers can fine-tune, deploy and invoke their customized model by accessing the one-stop MaaS with their own private dataset. In this paper, we identify the model and data privacy leakage risks in MaaS fine-tuning, and propose a Split-and-Privatize (SAP) framework, which manage to mitigate the privacy issues by adapting the existing split learning architecture. The proposed SAP framework is sufficiently investigated by experiments, and the results indicate that it can enhance the empirical privacy by 62% at the cost of 1% model performance degradation on the Stanford Sentiment Treebank dataset.
This work delves into the semantics of Old English lexical paradigms based on strong verbs. Its aim is to describe the patterns of semantic inheritance that hold in these paradigms, which present morphologically related words sharing the form and meaning of the base of derivation. The analysis carried out permits to circumscribe semantic derivation in these Old English lexical paradigms into the lexical entailment relations of troponymy, -troponymy, backward presupposition and cause, and the semantic relations of synonymy and opposition. The data of research has been retrieved from the lexical database Nerthus (Martín Arista et. al 2016). On the theoretical side, this examination follows the English lexical database WordNet (Princeton, 2010). This research unfolds a systematic methodology that is thoroughly described and illustrated by means of the paradigm (ge)berstan. It has not only been possible to circumscribe semantic derivation into the six semantic relations mentioned, but also to determine their frequency of occurrence in the paradigms under analysis, which indicates that synonymy and troponymy are the most recurrent semantic relations.
The growing interaction between humans and machines raises the necessity to more sophisticated tools for natural language understanding. Dependency parsing is crucial for capturing the semantics of a sentence. Although graph-based dependency parsing approaches outperform transition-based methods because they are not exposed to error propagation as their compeer, their feature space is comparatively limited. Thus, the main issue with graph-based parsing is how to expand the set of features to improve performance. In this research, we propose to expand the feature space of graph-based parsers. To benefit from the global meaning of the entire sentence content, we employee the sentence representation as an additional token feature. Also, to highlight local word collaborations that build sub-tree structures, we use convolutional neural network layers over token embeddings. We achieve the state-of-art results for Turkish, English, Hungarian, and Korean by getting the unlabeled and labeled attachment scores respectively on the test sets; 82.64% and 76.35% on Turkish IMST, 93.36% and 91.34% on English EWT, 90.85% and 87.39% on Hungarian Szeged, 92.44% and 89.58% on Korean GSD treebanks. Our experimental findings show that augmented global and local features empower the performance of graph-based dependency parsers.
Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.
The specificities of Arabic parsing, such as agglutination, vocalization, and the relatively order-free words in Arabic sentences, remain major issues to consider. To promote its robustness, such parseing should define different types of constraints. Property Grammar (PG) formalism verifies the satisfiability of the constraints directly on the units of the structure, thanks to its properties (or relations). In this context, we propose to build a probabilistic parser with syntactic properties, using a PG, and we measure the production rules in terms of different implicit information and in particular the syntactic properties. We experimented with our parser on the treebank ATB, using the parsing algorithm CYK, and we obtained encouraging results. Our method is also automatic for implementation of most property types. Its generalization for other languages or corpus domains (using treebanks) could be a good perspective. Its combination with pre-trained models of BERT may also make our parser faster.
One of the most crucial Natural Language Processing (NLP) tasks is associated with the universality-driven development of language resources for different languages (e.g. Universal Dependencies (UD), UniMorph, PARSEME, etc.). This article describes the possibility of creating a Syntactic TreeBank for Georgian, and consists of four sections. The first section briefly describes different linguistic resources that exist concerning Georgian and mentions the importance of syntactic annotation. The second section focuses on the tools used for mapping the existing targets for Georgian to the UD format. The third section includes a description of the principles of syntactic annotation and language-specific documentation files, while the fourth section summarizes the work done and describes the future stages of the development of the syntactic TreeBank.
Linguistic heterogeneity and fluidity – prominently captured in the notion of ‘translanguaging’– are starting to be seen as normative and natural. In turn, homogeneity and fixity, instantiated for example in standard languages, are becoming the ‘odd-ones-out.’ I challenge the dichotomy between linguistic fluidity (languaging) and fixity (named languages), in a situated conceptual account of translingual writing practices in English classrooms in a Khayelitshan primary school. These spaces fold the linguistic fluidity typical of South African townships, and the fixity of two standard languages, into one complex spatial repertoire. Operationalizing this spatial perspective, I suggest that students are constantly engaged in relanguaging, recursively sorting out the classroom repertoire according to the various linguistic norms enfolded in the space, and of bringing together linguistic resources in various combinations. Relanguaging systematically unsettles the dichotomy between fluid languaging and fix institutional language norms retained in dominant conceptualizations of translanguaging. This way it opens up new conceptual and analytical perspectives with possible pedagogical implication for writing instruction and testing. Standard English could, for example, be assessed beyond its own confines, using writing tasks that can make visible increasingly sophisticated linguistic sorting skills as students.