Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Recent theories propose moderate (compared to high or no) stressor exposure to promote emotion regulation capacities. More precisely, stressful situations are expected to serve as practice opportunities for cognitive reappraisal (CR), that is, the reinterpretation of a situation to alter its emotional impact. Accordingly, in this study, we expect an inverted U-shaped relationship between exposure to daily hassles and performance in a CR task, that is, best reappraisal ability in individuals with a history of moderate stressor exposure. Participants (N = 165) reported the number of daily hassles during the last week as indicator of stressor exposure and completed the Script-based Reappraisal Test (SRT). In the SRT, participants are presented with fear-eliciting scripts and instructed to either downregulate negative affect via reappraisal (reappraisal-trials) or react naturally (control-trials). Two measures indicate CR ability: (1) reappraisal effectiveness, that is, the difference between affective ratings in reappraisal- and control-trials and (2) reappraisal inventiveness, that is, the number of valid and categorically different reappraisal thoughts. Multiple regression analyses revealed positive linear, but not quadratic, relationships of exposure to daily hassles and both indicators of CR ability. Potential benefits of stressor exposure for emotion regulation processes are discussed.
Previously we found perspective taking (PT) influenced affect ratings of negative pictures more than neutral pictures. The current follow-up experiments extend that research to explore effects of perspective taking with positive valence pictures. We used stimuli consisting of neutral, happy and sad pictures. Stimuli were presented either mixed within blocks (Experiment 1) or separated by emotion (neutrals + happy/sad) into two separate blocks (Experiment 2). Participants rated (from 1- to 7 based on emotional strength) stimuli from different perspectives (sensitive/tough/their own, i.e., "me"). Emotional strength rating was a dependent variable. A significant interaction between valence and PT was found in both experiments. The difference between adopting sensitive and tough perspectives toward sadness was larger than toward the neutral condition, replicating our results from the previous study. The same difference (sensitive-tough) was larger toward the happiness condition than toward the neutral one (this was a trend in Experiment 1 and was significant in Experiment 2) and toward the sadness condition than toward the happy one. These results suggest that PT effects on emotional ratings are modulated by valence of stimuli.
We present an initial study into the representation of tree-adjoining grammar formalism for parsing Manipuri language. Being a low resource and computationally less researched language, it is difficult to achieve a natural language parser for Manipuri. Treebanks, which are the main requirement for inducing data-driven parsers, are not available for Manipuri. In this paper, we present an extensive analysis of the Manipuri language structure and formulate a lexicalized tree-adjoining grammar. A generalized structure of Manipuri phrases, clauses and the structure of basic and derived sentences have been presented. The sentence types covered in our analysis are that of simple, compound and complex sentences. Using the tree-adjoining grammar we have formulated, one can implement a Manipuri parser whose results can be of immense help in creating a Treebank for Manipuri.
Music tempo is closely connected to listeners’ musical emotion and multifunctional neural activities. Music with increasing tempo evokes higher emotional responses and music with decreasing tempo enhances relaxation. However, the neural substrate of emotion evoked by dynamically changing tempo is still unclear. To investigate the spatial connectivity and temporal dynamic functional network connectivity (dFNC) of musical emotion evoked by dynamically changing tempo, we collected dynamic emotional ratings and conducted group independent component analysis (ICA), sliding time window correlations, and k-means clustering to assess the FNC of emotion evoked by music with decreasing tempo (180–65 bpm) and increasing tempo (60–180 bpm). Music with decreasing tempo (with more stable dynamic valences) evoked higher valence than increasing tempo both with stronger independent components (ICs) in the default mode network (DMN) and sensorimotor network (SMN). The dFNC analysis showed that with time-decreasing FNC across the whole brain, emotion evoked by decreasing music was associated with strong spatial connectivity within the DMN and SMN. Meanwhile, it was associated with strong FNC between the DMN–frontoparietal network (FPN) and DMN–cingulate-opercular network (CON). The paired t -test showed that music with a decreasing tempo evokes stronger activation of ICs within DMN and SMN than that with an increasing tempo, which indicated that faster music is more likely to enhance listeners’ emotions with multifunctional brain activities even when the tempo is slowing down. With increasing FNC across the whole brain, music with an increasing tempo was associated with strong connectivity within FPN; time-decreasing connectivity was found within CON, SMN, VIS, and between CON and SMN, which explained its unstable valence during the dynamic valence rating. Overall, the FNC can help uncover the spatial and temporal neural substrates of musical emotions evoked by dynamically changing tempi.
Dutch reading culture is so international that it is fair to say foreign texts in Dutch translation are part of Dutch literature. But translation of literature ‘into Dutch’ is itself not without pitfalls, it proves to be an arena where Dutch diverging linguistic norms become visible. Retranslation can be a means to negotiate these complex target culture norms. In the Dutch language literary field the policy of avoiding ‘Flemish’ for the Dutch language book market seems to have been consistent and widespread as e.g. the case of Richard Scarry’s ABC-books shows. Three consecutive translations of Rudyard Kipling’s The Jungle Books, seen from a cultural-political angle, clearly show how the unity or heterogeneity of the Dutch speaking literary field is negotiated. My paper demonstrates how retranslations can serve as negotiations between not only source and target culture but even within the target culture itself.
Civil identity is one of the most significant factors in modern political practice. Today’s identity formation and development of large national groups is less based on a cultural and historical foundation and increasingly depends on political technologies. Among them, the construction of new languages plays an important role. The article studies the Bosnian language policy, which, contrary to forming a common civil identity, as a result of the politicization of linguistic norms becomes a factor in creating a “forge of hatred”. Drawing on constructivist social theories, the author summarizes Bosnian linguistic practices and examines them through the prism of symbolic interactionism and negative feedback systems. Particular attention is paid to situations when the desire for effective communication motivates speakers to abandon ethnically colored linguistic markers and situations in which the language acts as a defense against the internal “other.” Applying the criteria for distinguishing between language and dialects, the author concludes that the phonetic principle of the Serbo-Croatian language formation made it possible, after the destruction of Yugoslavia, to turn this linguistic continuum into an identification weapon to delimit the citizens of one country. This experience helps analyze the politicization of literary interpretations and linguistic norms in other regions of the world, where there are also examples of the growth of xenophobia, nationalism, and intolerance resulting from a differentiating language policy.
Research on and the development of technology that promotes human subjective well-being is crucial for the current global information society. Focusing on positive psychological intervention in social communication through mobile devices, this study proposes an input method to promote subjective well-being by recommending positive words and phrases for their negative counterparts. Accordingly, a design workshop was conducted to develop a reframing dictionary in Japanese. This dictionary constitutes a collection of negative words and their corresponding positive words and phrases that convey the same meaning. We also developed an input method that encourages users to select positive words and phrases when they enter a negative word. Preliminary evaluation results indicate a significant difference in positive affect ratings before and after communicating on social networking sites using the proposed input method. Thus, this input method contributes to promoting psychological well-being during daily information activities.
Abstract Graphs have become an increasingly important means of representing data, for instance, when communicating data on climate change. However, graph characteristics might significantly affect graph comprehension. The goal of the present work was to test whether the marking forms usually depicted on line-graphs, can have an impact on graph evaluation. As past work suggests that triangular forms might be related to threat, we compared the effect of triangular marking forms with other symbols (triangles, circles, squares, rhombi, and asterisks) on subjective assessments. Participants in Study 1 ( N = 314) received 5 different line-graphs about climate change, each of them using one out of 5 marking forms. In Study 1, the threat and arousal ratings of the graphs with triangular marking shapes were not higher than those with the other marking symbols. Participants in Study 2 ( N = 279) received the same graphs, yet without labels and indeed rated the graphs with triangle point markers as more threatening. Testing whether local rather than global spatial attention would lead to an impact of marker shape in climate graphs, Study 3 ( N = 307) documented that a task demanding to process a specific data-point on the graph (rather than just the line graph as a whole) did not lead to an effect either. These results suggest that marking symbols can principally affect threat and arousal ratings but not in the context of climate change. Hence, in graphs on climate change, choice of point markers does not have to take potential side-effects on threat and arousal into account. These seem to be restricted to the processing of graphs where form aspects face less competition from the content domain on judgments.
Although there are numerous studies on collocation in English writing by L2 university students, little is known about the problems encountered by mature researchers writing authentic L2 English texts in their fields. This study investigates collocation issues in L2 English research papers in Brazil. Its starting point was the compilation of the Brazilian Academic Corpus of English (BrACE), a 906,035-word multidisciplinary corpus of journal articles written in English that have been published in Brazilian journals. The most frequent noun collocations in this corpus were contrasted with the expert writing lexical database underlying the ColloCaid academic writing assistant. No evidence of systematic miscollocation was found in the published papers represented in BrACE. However, many general academic English collocations were conspicuous by their absence from BrACE, including collocations with L1 Portuguese cognates. We also observed that the collocations in BrACE were less diverse and tended to score higher in terms of strength of association than their equivalents in the reference data. In addition to feedback on miscollocations which might arise in unedited manuscripts, our findings to conclude that Brazilian (and other English L2) research writers can benefit from suggestions to expand their collocation repertoire, enhance their perceptions of collocation strength, and offset collocation avoidance.
Theimplicit discourse relation classification is of great importance to discourse analysis. It aims to identify the logical relation between sentence pair. Compared with the linear network model, the graph neural network has a more complex structure to capture cross-sentence interactions. Therefore, this article proposes a semantic graph neural network for implicit discourse relation classification. Specifically, we design a semantic graph to describe the syntactic structure of sentences and semantic interactions between sentence pair. Then, convolutional neural network (CNN) with different convolutional kernels to extract the multi-granularity semantic features. The experimental results on Penn Discourse TreeBank 2.0 (PDTB 2.0) prove that our work performed well.
OBJECTIVES: To determine the diagnostic accuracy of dual-energy CT (DECT) virtual noncalcium (VNCa) reconstructions for assessing thoracic disk herniation compared to standard grayscale CT. METHODS: In this retrospective study, 87 patients (1131 intervertebral disks; mean age, 66 years; 47 women) who underwent third-generation dual-source DECT and 3.0-T MRI within 3 weeks between November 2016 and April 2020 were included. Five blinded radiologists analyzed standard DECT and color-coded VNCa images after a time interval of 8 weeks for the presence and degree of thoracic disk herniation and spinal nerve root impingement. Consensus reading of independently evaluated MRI series served as the reference standard, assessed by two separate experienced readers. Additionally, image ratings were carried out by using 5-point Likert scales. RESULTS: MRI revealed a total of 133 herniated thoracic disks. Color-coded VNCa images yielded higher overall sensitivity (624/665 [94%; 95% CI, 0.89-0.96] vs 485/665 [73%; 95% CI, 0.67-0.80]), specificity (4775/4990 [96%; 95% CI, 0.90-0.98] vs 4066/4990 [82%; 95% CI, 0.79-0.84]), and accuracy (5399/5655 [96%; 95% CI, 0.93-0.98] vs 4551/5655 [81%; 95% CI, 0.74-0.86]) for the assessment of thoracic disk herniation compared to standard CT (all p <.001). Interrater agreement was excellent for VNCa and fair for standard CT (ϰ = 0.82 vs 0.37; p <.001). In addition, VNCa imaging achieved higher scores regarding diagnostic confidence, image quality, and noise compared to standard CT (all p <.001). CONCLUSIONS: Color-coded VNCa imaging yielded substantially higher diagnostic accuracy and confidence for assessing thoracic disk herniation compared to standard CT. KEY POINTS: • Color-coded VNCa reconstructions derived from third-generation dual-source dual-energy CT yielded significantly higher diagnostic accuracy for the assessment of thoracic disk herniation and spinal nerve root impingement compared to standard grayscale CT. • VNCa imaging provided higher diagnostic confidence and image quality at lower noise levels compared to standard grayscale CT. • Color-coded VNCa images may potentially serve as a viable imaging alternative to MRI under circumstances where MRI is unavailable or contraindicated.
There is empirical evidence in different languages on how the computation of gender morphology during psycholinguistic processing affects the conformation of sex-generic representations. However, there is no empirical evidence on the processing of non-binary morphological variants in Spanish (-x or -e) in contrast to the generic masculine variant (-o). To analyze this phenomenon, we conducted two experiments: an acceptability judgment task and a sentence comprehension task. The results show differences depending on the task. So, the underlying processes that are put into play in each one generate different effects. In acceptability judgments, which involve strategic processes mediated by beliefs and the linguistic norm, the generic masculine is more acceptable to refer to mixed groups. In the sentence comprehension task, which inquires about automatic processes and implicit representations, the non-binary forms consistently elicited a reference to mixed groups. Furthermore, the response times indicated that these morphological variants do not entail a higher processing cost than the generic masculine.
The basic prerequisite for using any language is the willingness of the speaker to follow the rules of the game. Socially defined norms of language use then tend to set the limits within which one can express oneself using this language. Whether these norms set the speaker free or whether they act as constraints in a free expression of Self, is a question that will be raised in this article. Using examples from Hindi, the paper highlights the role of such norms of language use in perpetuating gender stereotypes. Gender stereotypes get constructed as part of a broader process of social differentiation but the site of this construction is to a large extent the normal everyday discourse. A normal classroom discussion amongst university students in New Delhi thus shows how deep rooted such stereotypes are and how effectively they get perpetuated through language and linguistic norms in Indian society. The basic premise in this paper is that meanings are context-specific, they are not fixed and they get created in discourse. But since language use is one thread in social fabric, it serves as an instrument to construct and perpetuate gender stereotypes. The paper is more of an essay on issues that became obvious about gender stereotypes during two classroom discussions. It should not therefore be taken as a study into the deeper aspects of gender representation in Hindi.
The mood induction paradigm has been an important tool for investigating the effects of negative emotional states on working memory (WM) executive functions. Though some evidence showed that negative mood has a differential effect on verbal and visuospatial WM, other findings did not report a similar effect. To explore this issue, we examined the negative mood's impact on verbal and visuospatial WM executive tasks based on grammatical reasoning and visuospatial rotation. Participants with no anxiety or depression disorders performed the tasks before and after negative (n = 14) or neutral (n = 13) mood induction. Participants' mood at the beginning and the end of the session was assessed by the Present Mood States List (LEAP) and word valence rating. The analyses showed changes in the emotional state of the negative group (ps <.03) but not of the neutral group (ps >.83) in the LEAP instrument. No significant differences between groups were observed in the WM tasks (ps >.33). Performance in the visuospatial WM task improved after mood induction for both groups (p <.05), possibly due to a practice effect. In sum, our findings challenge the view that negative mood modulates WM executive functions; thus, they were discussed considering the similarities and differences between studies that found negative mood effects on WM and those that did not find. Different WM tasks tap distinct processes and components, which may underlie behavioral effects of negative mood on WM tasks.
The article deals with the problem of improving the quality of machine-based translation. The paper provides the neural network and statistical approaches with control over the created automatic specialized dictionaries for the development of a system for the automatic translation of English scientific and technical texts on information technologies into Belarusian. The article considers the main aspects and stages of linguistic database and algorithmic model developing for the given purpose and analyses the results and prospects of the developed automated information system “English-Belarusian Dictionary”.
Laughter is a fundamental communicative signal in our relations with other people and is used to convey a diverse repertoire of social and emotional information. It is therefore potentially a useful probe of impaired socio-emotional signal processing in neurodegenerative diseases. Here we investigated the cognitive and affective processing of laughter in forty-seven patients representing all major syndromes of frontotemporal dementia, a disease spectrum characterised by severe socio-emotional dysfunction (twenty-two with behavioural variant frontotemporal dementia, twelve with semantic variant primary progressive aphasia, thirteen with nonfluent-agrammatic variant primary progressive aphasia), in relation to fifteen patients with typical amnestic Alzheimer's disease and twenty healthy age-matched individuals. We assessed cognitive labelling (identification) and valence rating (affective evaluation) of samples of spontaneous (mirthful and hostile) and volitional (posed) laughter versus two auditory control conditions (a synthetic laughter-like stimulus and spoken numbers). Neuroanatomical associations of laughter processing were assessed using voxel-based morphometry of patients' brain MR images. While all dementia syndromes were associated with impaired identification of laughter subtypes relative to healthy controls, this was significantly more severe overall in frontotemporal dementia than in Alzheimer's disease and particularly in the behavioural and semantic variants, which also showed abnormal affective evaluation of laughter. Over the patient cohort, laughter identification accuracy was correlated with measures of daily-life socio-emotional functioning. Certain striking syndromic signatures emerged, including enhanced liking for hostile laughter in behavioural variant frontotemporal dementia, impaired processing of synthetic laughter in the nonfluent-agrammatic variant (consistent with a generic complex auditory perceptual deficit) and enhanced liking for numbers ('numerophilia') in the semantic variant. Across the patient cohort, overall laughter identification accuracy correlated with regional grey matter in a core network encompassing inferior frontal and cingulo-insular cortices; and more specific correlates of laughter identification accuracy were delineated in cortical regions mediating affective disambiguation (identification of hostile and posed laughter in orbitofrontal cortex) and authenticity (social intent) decoding (identification of mirthful and posed laughter in anteromedial prefrontal cortex) (all p <.05 after correction for multiple voxel-wise comparisons over the whole brain). These findings reveal a rich diversity of cognitive and affective laughter phenotypes in canonical dementia syndromes and suggest that laughter is an informative probe of neural mechanisms underpinning socio-emotional dysfunction in neurodegenerative disease.
Sparse neural networks have been widely applied to reduce the necessary resource requirements to train and deploy over-parameterized deep neural networks. For inference acceleration, methods that induce sparsity from a pre-trained dense network (dense-to-sparse) work effectively. Recently, dynamic sparse training (DST) has been proposed to train sparse neural networks without pre-training a dense network (sparse-to-sparse), so that the training process can also be accelerated. However, previous sparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs) and Convolutional Neural Networks (CNNs), failing to match the performance of dense-to-sparse methods in Recurrent Neural Networks (RNNs) setting. In this paper, we propose an approach to train sparse RNNs with a fixed parameter count in one single run, without compromising performance. During training, we allow RNN layers to have a non-uniform redistribution across cell gates for a better regularization. Further, we introduce SNT-ASGD, a variant of the averaged stochastic gradient optimizer, which significantly improves the performance of all sparse training methods for RNNs. Using these strategies, we achieve state-of-the-art sparse training results with various types of RNNs on Penn TreeBank and Wikitext-2 datasets.
This paper takes a corpus-based approach and examines the linguistic properties of two Korean nominalizers -(u)m and -ki. From the Sejong Treebank corpus, all the sentences with -(u)m and -ki are extracted. Twenty linguistic factors are manually encoded into the extracted sentences. Then, all the encoded data are statistically analyzed with (binary) logistic regression. Although we take a monofactorial analysis, we obtain a good statistical model whose C value is 0.956. Through the analysis, the followings are observed: (i) -(u)m and -ki are used with the ratio of 1:9 in Korean, (ii) among twenty linguistic factors, only ten factors are statistically significant, and (iii) not only the verbs which take -(u)m and -ki as a complement but also the verbs which merge with these two nominalizers also play important roles in the determination of nominalizers. (Chungnam National University·Kunsan National University)
We introduce Trankit, a light-weight Transformer-based Toolkit for\nmultilingual Natural Language Processing (NLP). It provides a trainable\npipeline for fundamental NLP tasks over 100 languages, and 90 pretrained\npipelines for 56 languages. Built on a state-of-the-art pretrained language\nmodel, Trankit significantly outperforms prior multilingual NLP pipelines over\nsentence segmentation, part-of-speech tagging, morphological feature tagging,\nand dependency parsing while maintaining competitive performance for\ntokenization, multi-word token expansion, and lemmatization over 90 Universal\nDependencies treebanks. Despite the use of a large pretrained transformer, our\ntoolkit is still efficient in memory usage and speed. This is achieved by our\nnovel plug-and-play mechanism with Adapters where a multilingual pretrained\ntransformer is shared across pipelines for different languages. Our toolkit\nalong with pretrained models and code are publicly available at:\nhttps://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also\navailable at: http://nlp.uoregon.edu/trankit. Finally, we create a demo video\nfor Trankit at: https://youtu.be/q0KGP3zGjGc.\n
Abstract This paper introduces, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalizes the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. We tested our method on the WSJ section of the Peen Treebank annotated with VerbNet and PropBank labels and on the Brown corpus. The evaluation has been performed according to the CoNLL Shared Task on Joint Parsing of Syntactic and Semantic Dependencies. The obtained precision, recall, and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM, and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall, and F1 measure.
Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs as bracket strings, or (iii) associating partial transition sequences of a transition-based parser to words. Yet, there is little understanding about how these linearizations behave in low-resource setups. Here, we first study their data efficiency, simulating data-restricted setups from a diverse set of rich-resource treebanks. Second, we test whether such differences manifest in truly low-resource setups. The results show that head selection encodings are more data-efficient and perform better in an ideal (gold) framework, but that such advantage greatly vanishes in favour of bracketing formats when the running setup resembles a real-world low-resource configuration.
OBJECTIVE: Research into echocardiography (echo) during cardiac arrest has suffered from methodological flaws that limit aggregation of findings. We developed and validated a novel image rating scale for qualitative analysis of echo images obtained during resuscitation. METHODS: A novel 5-point ordinal rating scale was developed and validated using recorded echo images from 145 consecutive cardiac arrest patients. Recorded echo images were reviewed in a blinded fashion by investigators experienced in cardiac arrest echo, and image quality was rated using this scale. Cardiac activity was subsequently classified as no activity, disorganized activity and organized activity. The primary outcome was inter-rater agreement using the image quality rating scale. Secondary outcome was the qualitative evaluation of the type of cardiac activity. RESULTS: A total of 235 ultrasounds were analyzed by study investigators using the image quality rating scale. The overall image quality agreement between reviewers using the scale was good with a weighted kappa of 0.65. Agreement for image quality in subxyphoid images was greater than in parasternal images (0.65-0.52). Echo analysis of cardiac activity showed no activity (33%), disorganized activity (18%), and organized activity (49%). Agreement was great for presence or absence of "cardiac activity" and "organized cardiac activity" with a kappa of 0.84 and 0.78. CONCLUSIONS: A novel image quality rating scale for echo during cardiac arrest demonstrates substantial agreement between reviewers. Agreement regarding the presence or absence, as well as the organization of cardiac activity was substantial.
Abstract In this chapter we describe a multilingual extension of Swedish FrameNet++, intended to address research questions of a broad comparative nature, in genealogical, areal and typological linguistics, focusing on the integration into Swedish FrameNet++ of so-called core vocabularies, used in several linguistic subfields in order to conduct massive comparative studies involving large numbers of languages. Specifically, we describe the inclusion of two such lexical databases covering several hundred South Asian languages, with the aim of investigating areal and genealogical connections among these languages.
Automated machine learning (AutoML) is a technique which helps to determine the optimal or near-optimal model for a specific dataset and has been a focused research area during the last years. The automation of model design opens doors for non-machine learning experts to utilize machine learning models in several scenarios, which is both appealing for a wide range of researchers and for cloud services as well. Neural Architecture Search is a subfield of AutoML where the optimal artificial neural network model's architecture is generally searched with adaptive algorithms. This paper proposes a method to apply Efficient Neural Architecture Search (ENAS) to LSTM-like recurrent architecture, which uses a gating mechanism an inner memory. Using this method, the paper investigates if the handcrafted Long Short-Term Memory (LSTM) cell is an optimal or near-optimal solution of sequence modelling for a given dataset, or other, automatically defined recurrent structures outperform. The performance of vanilla LSTM, and advanced recurrent architectures designed by random search, and reinforcement learning-based ENAS are examined and compared. The proposed methods are evaluated in a text generation task on the Penn TreeBank dataset.
Objective: Somatization symptoms are commonly comorbid with depression. Furthermore, people with depression and somatization have a negative memory bias. We investigated the differences in emotional memory among adolescent patients with depressive disorders, with and without functional somatization symptoms (FSS). Methods: We recruited 30 adolescents with depression and FSS, 38 adolescents with depression but without FSS, and 38 healthy participants. Emotional memory tasks were conducted to evaluate the emotional memory of the participants in the three groups. The clinical symptoms were evaluated using the Hamilton Depression Rating Scale (HDRS) and the Children's Somatization Inventory (CSI). Results: The valence ratings and recognition accuracy rates for positive and neutral images of adolescent patients were significantly lower than those of the control group ( F = 12.208, P &lt; 0.001; F = 6.801, P &lt; 0.05; F = 14.536, P &lt; 0.001; F = 6.306, P &lt; 0.05, respectively); however, the recognition accuracy rate for negative images of adolescent patients of depression without FSS was significantly lower than that of patients with FSS and control group participants ( F = 10.316, P &lt; 0.001). These differences persisted after controlling for HDRS scores. The within-group analysis revealed that patients of depression with FSS showed significantly higher recognition accuracy rates for negative images than the other types ( F = 5.446, P &lt; 0.05). The recognition accuracy rate for negative images was positively correlated with CSI scores ( r = 0.352, P &lt; 0.05). Conclusion: Therefore, emotional memory impairment exists in adolescent patients of depression and FSS are associated with negative emotional memory retention.
Current accounts of neural plasticity emphasize the role of connectivity and conserved function in determining a neural tissue’s functional role even after atypical early experiences. However, in apparent conflict with this view, studies have suggested that in congenitally blind individuals, language activates primary visual cortex, with no evidence of major changes in anatomical connectivity that could explain this apparent drastic functional change in what is typically a low-level visual area. To reconcile what appears to be unprecedented functional reorganization in V1 with known accounts of plasticity limitations, we used functional magnetic resonance imaging (fMRI) to test whether primary visual cortex also responds to spoken language in sighted individuals. We found that primary visual cortex was activated by comprehensible speech as compared to a reversed speech control task, in a left-lateralized and focal manner, in sighted individuals. Importantly, left V1 activation was also significant and comparable for abstract and concrete words, precluding a visual imagery account of such activation, and activation was also not correlated with attentional arousal ratings. Together these findings suggest that primary visual cortex responds to verbal information even in the typically developed brain, potentially to predict visual input. This capability might be the basis for the strong V1 language activation observed in people born blind, re-affirming the notion that plasticity is guided by pre-existing connectivity and abilities in the typically developed brain.
Abstract We present a hybrid HMM-based PoS tagger for Old Church Slavonic. The training corpus is a portion of one text, Codex Marianus (40k) annotated with the Universal Dependencies UPOS tags in the UD-PROIEL treebank. We perform a number of experiments in within-domain and out-of-domain settings, in which the remaining part of Codex Marianus serves as a within-domain test set, and Kiev Folia is used as an out-of-domain test set. Analysing by-PoS-class precision and sensitivity in each run, we combine a simple context-free n-gram-based approach and Hidden Markov method (HMM), and added linguistic rules for specific cases such as punctuation and digits. While the model achieves a rather non-impressive accuracy of 81% in in-domain settings, we observe an accuracy of 51% in out-of-domain evaluation, which is comparable to the results of large neural architectures based on pre-trained contextual embeddings.
The paper substantiates the relevance of dictionary culture formation as an important component of information culture and personal culture in general. In addition, the concept of "the culture of dictionary use" is defined. The vocabulary lesson is described as an innovative type of Russian lessons; this type is characterised by a specific goal and a specific content related to the personal, meta-subject, and subject levels of the results achieved in the process of education. Such lessons play a significant role in the development of students’ linguistic personality; they also stimulate learners’ cognitive activity, facilitate the mastering of linguistic norms and the acquisition of self-regulatory skills. Resource materials for a vocabulary lesson, namely vocabulary exercises, are demonstrated. Informational-orientational, activity-semantic, and activity-textual vocabulary tasks illustrate the proposed typology of vocabulary tasks.
BACKGROUND AND PURPOSE: The aim of this study was to assess whether the severity of tinnitus, as measured using ratings of tinnitus loudness, annoyance, and effect on life, was influenced by the lockdown related to the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH DESIGN: This was a retrospective study. STUDY SAMPLE: The data for 105 consecutive patients who were seen at a tinnitus clinic in an audiology department in the United Kingdom during the COVID-19 lockdown between April and June 2020 and 123 patients seen in the same period of the previous year, prior to the COVID-19 pandemic were included. DATA COLLECTION: Demographic data for the patients, results of their pure-tone audiometry, and their score on visual analog scale (VAS) of tinnitus loudness, annoyance, and effect on life were imported from their records held at the audiology department. This was a retrospective survey comparing ratings on the VAS of tinnitus loudness, annoyance, and effect on life for consecutive patients seen during the COVID-19 lockdown and consecutive patients seen in the same period of the previous year, prior to the COVID-19 pandemic. Patients seen prior to lockdown used a pen and paper version of the VAS, while the patients who were assessed during the COVID-19 lockdown used an adapted version of the VAS, via telephone. All patients were seeking help for their tinnitus for the first time. RESULTS: The mean scores for tinnitus loudness, annoyance, and effect on life did not differ significantly for the groups seen prior to and during lockdown. CONCLUSION: Any changes in psychological well-being or stress produced by the lockdown did not significantly affect ratings of the severity of tinnitus.
A differentiable neural computer (DNC) is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism. Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems. In this study, we apply a DNC to a language model (LM) task. The LM task is one of the reasoning problems, because it can predict the next word using the previous word sequence. However, memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory, which degrades performance. Therefore, we propose a forget gate-based memory deallocation (FMD) method, which searches for the minimum value of elements in a forget gate-based retention vector. The forget gate-based retention vector indicates the retention degree of information stored in each external memory address. In experiments, we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example. For LM tasks, we evaluated DNC using the Penn Treebank and enwik8 LM tasks. Although it does not yield SOTA results in LM tasks, the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character. For the speech recognition rescoring tasks, FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate.
While the highly multilingual Universal Dependencies (UD) project provides extensive guidelines for clausal structure as well as structure within canonical nominal phrases, a standard treatment is lacking for many "mischievous" nominal phenomena that break the mold. As a result, numerous inconsistencies within and across corpora can be found, even in languages with extensive UD treebanking work, such as English. This paper surveys the kinds of mischievous nominal expressions attested in English UD corpora and proposes solutions primarily with English in mind, but which may offer paths to solutions for a variety of UD languages.
Discourse information, as postulated by popular discourse theories, such as RST and PDTB, has been shown to improve an increasing number of downstream NLP tasks, showing positive effects and synergies of discourse with important real-world applications. While methods for incorporating discourse become more and more sophisticated, the growing need for robust and general discourse structures has not been sufficiently met by current discourse parsers, usually trained on small scale datasets in a strictly limited number of domains. This makes the prediction for arbitrary tasks noisy and unreliable. The overall resulting lack of high-quality, high-quantity discourse trees poses a severe limitation to further progress. In order the alleviate this shortcoming, we propose a new strategy to generate tree structures in a task-agnostic, unsupervised fashion by extending a latent tree induction framework with an auto-encoding objective. The proposed approach can be applied to any tree-structured objective, such as syntactic parsing, discourse parsing and others. However, due to the especially difficult annotation process to generate discourse trees, we initially develop a method to generate larger and more diverse discourse treebanks. In this paper we are inferring general tree structures of natural text in multiple domains, showing promising results on a diverse set of tasks.
This study investigated the associations of imageability with fear reactivity. Imageability ratings of four word classes: positive and negative (i) emotional and (ii) propriosensitive, neutral and negative (iii) theoretical and (iv) neutral concrete filler, and fear reactivity scores—degree of fearfulness towards different situations (Total Fear (TF) score) and total number of extreme fears and phobias (Extreme Fear (EF) score), were obtained from 171 participants. Correlations between imageability, TF and EF scores were tested to analyze how word categories and their valence were associated with fear reactivity. Imageability ratings were submitted to recursive partitioning. Participants with high TF and EF scores had higher imageability for negative emotional and negative theoretical words. The correlations between imageability of negative emotional words and negative theoretical words for EF score were significant. Males showed stronger correlations for imageability of negative emotional words for EF and TF scores. High imageability for positive emotional words was associated with lower fear reactivity in females. These findings were discussed with regard to negative attentional bias theory of anxiety, influence on emotional systems, and gender-specific coping styles. This study provides insight into cognitive functions involved in mental imagery, semantic competence for mental imagery in relation to fear reactivity, and a potential psycholinguistic instrument assessing fear reactivity.
The focus of this paper is the impact of English observed in the language of an international magazine Cosmopolitan. The research was conducted taking into account three language versions of the monthly magazine: Russian, Polish and Spanish. Factual material was excerpted from the periodicals published in 2017–2021. Taking up this topic stems from the need to fill the gap in research on the language of luxury magazines, which have a great influence on forming the canons of linguistic norms and the linguistic awareness of their readers. The aim of the study is to analyze the collected Anglicisms (mainly loanwoard) in terms of their function, way of adaptation and presentation in the text space. Determining the reasons for the popularity of foreign forms in a given language space is also an important point of analysis. An additional assumption of the publication is to indicate the connections between the use of borrowings and the ideological concept of the magazine with cosmopolitanism.
The most straightforward approach to joint word segmentation (WS), part-of-speech (POS) tagging, and constituent parsing (PAR) is converting a word-level tree into a char-level tree, which, however, leads to two severe challenges. First, a larger label set (e.g., 600) and longer inputs both increase computational cost. Second, it is difficult to rule out illegal trees containing conflicting production rules, which is important for reliable model evaluation. If a POS tag (like VV) is above a phrase tag (like VP) in the output tree, it becomes quite complex to decide word boundaries. To deal with both challenges, this work proposes a two-stage coarse-to-fine labeling framework for joint WS-POS-PAR. In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i.e., phrase, subphrase, word, subword). The tree is guaranteed to be legal via constrained CKY decoding. In the fine labeling stage, the model expands each coarse label into a final label (such as VP, VP *, VV, VV * ). Experiments on Chinese Penn Treebank 5.1 and 7.0 show that our joint model consistently outperforms the pipeline approach on both settings of without and with BERT, and achieves new state-of-the-art performance.
This paper deliberates on the process of building the first constituency-to-dependency conversion tool of Turkish 1. The starting point of this work is a previous study in which 10,000 phrase structure trees were manually transformed into Turkish from the original Penn Treebank corpus. Within the scope of this project, these Turkish phrase structure trees were automatically converted into UD-style dependency structures, using both a rule-based algorithm and a machine learning algorithm specific to the requirements of the Turkish language. The results of both algorithms were compared and the machine learning approach proved to be more accurate than the rule-based algorithm. The output was revised by a team of linguists. The refined versions were taken as gold standard annotations for the evaluation of the algorithms. In addition to its contribution to the UD Project with a large dataset of 10,000 Turkish dependency trees, this project also fulfills the important gap of a Turkish conversion tool, enabling the quick compilation of dependency corpora which can be used for the training of better dependency parsers.
Although cognitive control and emotional control have been proposed to rely on similar processes, their specific relationship is not well understood. Given that reappraisal to down-regulate negative emotions requires inhibiting or limiting the expression of a prepotent appraisal of a situation in favor of selecting an alternative appraisal, inhibitory control seems to be a particularly relevant process. However, inconsistent findings on the relationship between inhibitory control and reappraisal ability have been reported, likely because of the application of single task measures in relatively small samples. Therefore, this study examined the relationship between both control processes using a powerful within-subject design in a large sample (N = 190) and by applying a battery of six commonly used inhibitory control tasks. Emotion regulation was measured comprehensively by self-reported habitual use of reappraisal and suppression strategies, by valence and arousal ratings during a reappraisal task and by concurrent physiological measures such as corrugator electromyography (EMG), skin conductance response (SCR), and heart period (HP). Frequentist and Bayesian analyses indicated that inhibitory control was not associated with emotion regulation in our sample of young healthy adults. Furthermore, by implementing a full two-by-two design including a “regulate neutral” condition, the present study provides evidence that domain-general regulation effects need to be separated from domain-specific regulation effects. Finally, compared to HP and SCR, corrugator EMG emerged as a suitable peripheral physiological indicator of regulatory success that was indicative of the regulation of negative emotion.
The benefits of applied linguistics This paper offers examples from three areas of applied linguistics showing that the relation between linguistics and applied linguistics is bidirectional: not only does applied linguistics utilize the results of linguistic research but descriptive and theoretical linguistics too can rely on applied linguistics. The huge databases established and managed by language technology can reveal hidden correlations within the language system. The area of applied linguistics known in Hungary as nyelvmvels 'language cultivation', which offers advice on issues concerning the (more fastidious) linguistic norm, is an important source for diachronic linguistics, as non-linguists who point out newly emerging "linguistic errors" (and the linguists who respond to them) are the first to observe the changes taking place in the language. Language acquisition research, i.e. the study of the language use of younger children, may call attention to linguistic constraints that go unnoticed in the language of adults.
The purpose of the study was to investigate the effects of passive recovery with self-selected time on affect, ratings of perceived exertion, and heart rate in self-selected interval exercises (SSIE). Fifteen older women (68.1 ± 3.8 years), weekly practitioners of functional activities participated in three SSIE with self-selected recovery time (SSRT) and one self-selected continuous exercise session, all at 24 min approximately. The SSIE had the following configurations: 1'/SSRT, 1.5'/SSRT, and 2'/SSRT. The results showed that at the beginning of stimulus heart rate in 1.5'/SSRT (107.9 ± 16.5) and 2'/SSRT (114.6 ± 17.1) were significantly greater (p <.05) compared with self-selected continuous exercise (102.8 ± 14.5). The ratings of perceived exertion in self-selected continuous exercise (2.4 ± 0.4; p <.05) were higher compared with SSIE in recovery. No significant differences were found in affect. The SSIE provided similar responses based on recoveries manipulations.
This paper explores quantitative results based on theoretical assumptions related to the predictions on N-merge systems (Rizzi 2016) ranked from minimum to a maximum of complexity in terms of the computational devices and derivational operations they require. We investigate the nature of external arguments focussing on 2-merge systems (two elements of the lexicon merge and the created unit is again merged with a further element directly extracted from the lexicon) and 3-merge systems (merge two elements created by previous operations of merge). We add a quantitative dimension to the established qualitative dimension discussed in the theory (Rizzi 2016) by investigating large-scale corpora representative of three populations of speakers: adult grammar (102 treebanks/101 languages), typically developing children (2 corpora/English and Chinese) and children with atypical development (1 corpus). The results confirm the predictions in Rizzi (2016): every language in our data set exploits 3-merge systems and less complex systems are the preferred options in early grammars.
The problem of visual pollution in the Philippines has been increasingly evident, and people are becoming aware of it. But to create effective solutions, a deep understanding of the problem should first be established. This paper was aimed to identify, analyze, and measure the visual pollution present in Intramuros, a heritage city in the Philippines that encapsulates the Philippine colonial architecture in the 1890s. The site is known for its preservation of its city image but also modern landscape changes. To achieve the goal, the application of the Indirect and Direct Method of Landscape Evaluation was executed. These methods led to two results: (1) the identification of components— which are landscape attributes and indicators, that make up a visual landscape; and (2) the understanding of how it is perceived by the observer through a survey and interviews, which are quantified by ratings. To further understand the relationship of indicators and ratings with each other, a series of correlational studies was done. This resulted to the establishment of Disturbance, Stewardship, and Image Rating as the primary descriptors of visual pollution. A weighted average formula was then established, which quantified the visual pollution of Intramuros through indicator values and response ratings. It was concluded that visual pollution in Intramuros, through research-based methodology, can be identified, analyzed, and measured. Specific viewpoints in the district were identified as unacceptably visually-polluted. Magallanes St. cor. Victoria St. in Intramuros had the highest VP Score at -4.886. Elements that contributed to visual pollution were also identified.
Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus.
Reducing negative impacts of stress, for example through mindfulness training, benefits physical and psychological well-being, and is becoming ever more crucial due to large-scale societal uncertainties (e.g., COVID-19). While extensive research has focused on mindfulness-related reductions in self-reported negativity, essentially no research has targeted task-based behavioral outcomes throughout long-term mindfulness trainings. Responses to emotionally ambiguous signals (e.g., surprised expressions), which might be appraised as either positive or negative, provide a nuanced assessment of one’s emotional bias across diverse contexts, offering unique leverage for assessing the effects of mindfulness. Here, we compared the effects of short- and long-term training via Mindfulness-Based Stress Reduction on ratings of faces with a relatively clear (angry, happy) and ambiguous (surprised) valence. Ratings became more positive for ambiguity from the start (Week 1) to end of training (Week 8; p &lt;.001), but there were no short-term effects (from a single class session). This shift towards positivity continued through an additional eight-week follow-up (Week 16; p &lt;.001). Notably, post-training valence bias (Week 8) was uniquely predicted by the non-reactivity facet of mindfulness (p =.01). Together, mindfulness promotes a relatively long-lasting shift toward positivity bias, which is uniquely supported by reduced emotional reactivity.
The main idea of the present paper is to uncover the hidden potential of Karachay-Balkar language in Constrained Writing. Being well described and deeply analyzed in terms of major linguistics subdisciplines, Karachay-Balkar language (as well as many other minor languages) lacks academic attention towards creation of experimental forms, e.g., palindromes. The present paper aims to answer the question "How big is the potential of Karachay-Balkar language in creating palindromes of different kinds, and which methods are applicable in doing that?" In order to answer this question, we justify choosing the appropriate lexical databases, and demonstrate that two different approaches are effective: "heuristic method", based on some apriori knowledge about Karachay-Balkar language and our own creativity; and "algorithmic method", based on some set of rules executed on Python. As a result, we have generated many palindromes of different kinds (letter-palindromes, syllable-palindromes, educanto-palindromes, "magic squares"), and about 70 of them are demonstrated in the present paper.
Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high- resource languages. Building language mod- els and, more generally, NLP systems for non- standardized and low-resource languages remains a challenging task. In this work, we fo- cus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi, found mostly on social media and messaging communication. In this low-resource scenario with data display- ing a high level of variability, we compare the downstream performance of a character-based language model on part-of-speech tagging and dependency parsing to that of monolingual and multilingual models. We show that a character-based model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank of this language leads to performance close to those obtained with the same architecture pre- trained on large multilingual and monolingual models. Confirming these results a on much larger data set of noisy French user-generated content, we argue that such character-based language models can be an asset for NLP in low-resource and high language variability settings.
<ns4:p>Approach biases to foods may explain why food consumption often diverges from deliberate dietary intentions. When cognitive resources are depleted, implicit responses may contribute to overeating and overweight. Yet, the assessment of behavioural biases with the approach-avoidance tasks (AAT) is often unreliable. We previously addressed methodological limitations of the AAT by employing naturalistic approach and avoidance movements on a touchscreen (hand-AAT) and instructing participants to respond based on the food/non-food distinction. In the consistent block, participants were instructed to approach food and avoid objects while in the inconsistent block, participants were instructed to avoid foods and approach objects. Biases were highly reliable but affected by the order in which participants received the two task blocks. In the current study, we aimed to resolve the block order effects by increasing the number of blocks from two to six and validate the hand-AAT with the implicit association task (IAT) and self-reported eating behaviours. We replicated the presence of reliable approach biases to foods and further showed that these were not affected by block order. Evidence for validity was mixed: biases correlated positively with external eating, food craving and aggregated image valence ratings but not with within-participants differences in desire to eat ratings of the images or the IAT. We conclude that hand-AAT can reliably assess approach biases to foods that are relevant to self-reported eating patterns and were not probably confounded by block-order effects.</ns4:p>
Quantitative studies of historical syntax require large amounts of syntactically annotated data, which are rarely available.The application of NLP methods could reduce manual annotation effort, provided that they achieve sufficient levels of accuracy.The present study investigates the automatic identification of chunks in historical German texts.Because no training data exists for this task, chunks are extracted from modern and historical constituency treebanks and used to train a CRF-based neural sequence labeling tool.The evaluation shows that the neural chunker outperforms an unlexicalized baseline and achieves overall F-scores between 90% and 94% for different historical data sets when POS tags are used as feature.The conducted experiments demonstrate the usefulness of including historical training data while also highlighting the importance of reducing boundary errors to improve annotation precision.
Abstract This paper is a corpus-based study of the various forms and uses of clefts in Naija, the largest West-African English lexifier pidgincreole, spoken in Nigeria and its diaspora as a second language by close to 100 million speakers. The data on which this paper is based is taken from the 500,000 word ANR-NaijaSynCor corpus, consisting of 300 samples of spontaneous speech, recorded in 2017 in 13 different locations in Nigeria, from 330 different speakers of both sexes, of various ages, education levels, and geographic origins. The quantitative data is taken from a sub-section of 9,621 sentences (almost 150,000 tokens) that constitute a syntactic treebank mirroring the social and geographic sampling of the full corpus. Clefts, pseudo-clefts and reverse pseudo- clefts are examined. Four types of clefts are described: wey-clefts, bare clefts, double clefts and zero-copula clefts. The properties of those clefting patterns are represented using a UD-type annotation scheme named SUD for Surface-Syntactic Universal Dependencies. The quantitative analysis of the data and comparison with former descriptions of the language underline the massive domination of bare clefts, and the emergence, among these various patterns, of a relative pronoun nãĩ “who/which” used only with cleft constructions, while the relativiser wey is being abandoned and specialises as relative clause operator.
This study examines the linguistic complexity of Spanish as a second language (L2) in learners' essays across proficiency levels at two timelines of a composition class during a college semester. Data comes from 22 L2 learners of Spanish enrolled in two sections of a third-year composition class at the college level, who were assigned nine compositions (150–250 words each) at different times throughout one semester. Data was prepared using a natural language processing (NLP) pipeline, including UDPipe, an NLP tool that allows annotation, part-of-speech (POS) tagging, lemmatization, and dependency parsing based on Universal Dependencies (UD) treebanks. In addition, the NLTK package and several Python scripts were used on the annotated model to process and extract syntactic and lexical information from the datasets. Results showed that selected predictors of syntactic complexity increased at different stages in the semester and the effect seems more robust in beginner learners. Moreover, the use of lower frequency lexicon appears to be integrated thorough out the semester in both groups of learners. These findings indicate the pedagogical benefits of Spanish composition courses and the specific indices of L2 writing development obtained during a semester of classes across two groups of language proficiency.