Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Recently, Chinese Syntax-aware Semantic Role Labeling (SRL) has attracted the attention of many researchers because syntax information is related to semantic role labeling intuitively. In this paper, we integrate constituency representation into SRL by combining multiple methods into a unified model. Specifically, our proposed model combine three methods for integrating constituent into SRL, including pipeline (use syntax structure), hard parameter (share encoder), implicit representation integration (not share encoder). We verify the effect of our model on Chinese Proposition Treebank (CPB) 1.0 dataset and conduct some ablation experiments to verify the impact of various parts of the model.
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 <.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 <.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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 Individuals' emotions have been studied for nearly half a century, but the literature has not advanced to the point of estimating the intensity of a stimulus capable of influencing valence and arousal. The aim of this study was to elucidate three new thresholds: the valence thresholds, represented by the compromised pleasure threshold (CPT) and unpleasure threshold (UT), and the arousal threshold (AT). Valence and arousal ratings were obtained through the affective slider (AS), and CPT, UT, and AT were determined for images of moldy Brazilian carrot cake. Results showed CPT occurs after 1.9 days of deterioration and the AT is reached after 10.5 days of deterioration. The moldy carrot cake was valenced negatively, in the low‐arousal region. The methodology was shown to be appropriate for measuring emotion thresholds, which highlights its potential to generate deeper understanding of consumers' perceptions of valence and arousal. Practical Applications When it comes to emotion‐driven food choices, emotion thresholds methodology can help in the monitoring of unhealthy food choices, because it will be able to provide thresholds corresponding to variables capable of significantly influencing the consumer's mood. Likewise, they may also be useful to industry, public policymakers, and health professionals, in order to facilitate the identification of the intrinsic and extrinsic factors that improve the mood of the consumer. Mitigation efforts can focus on the relationship between eating disorders and negative emotional states, which affect CPT, UT, and AT and, in turn, decision‐making. We propose that identifying the psychophysiological reactions to comforting stimuli allows us to examine differences in food processing cues among individuals with eating disorders (e.g., compulsive eating, anorexia, and bulimia), and how they shape emotion thresholds. This creates opportunities for psychoeducational interventions and improvements in decision‐making.
Natural speech contains many sources of acoustic variability both within and between talkers, which challenges speech recognition in some contexts but may facilitate language understanding in novel listening situations. Despite this ubiquitous variability, most previous studies that have examined the ability to extract sound patterns in speech—known as statistical learning—have used highly controlled, artificial, monotonic streams of syllables. Thus, it is unknown whether variability in speech may help or hinder statistical learning – an important question to resolve if statistical learning does indeed play a role in the segmentation of naturally spoken language, as widely theorized. Here, we assessed whether the use of naturally produced, variable speech sounds produced by multiple talkers benefits or impairs statistical learning, including the ability to generalize patterns to a novel talker. During training, participants listened to approximately 12 minutes of continuous speech made up of repeating trisyllabic words, spoken either by a single talker (single-talker condition) or four talkers speaking for three minutes each (multiple-talker condition). Post-training, all participants completed three assessments of learning: (1) an explicit familiarity rating task, (2) an explicit forced-choice recognition task, and (3) an implicit syllable target detection task. Results indicated that participants in both training conditions showed evidence of statistical learning across all assessments, providing an important demonstration that statistical learning is robust to additional variability in the speech signal. Further, in both the forced-choice recognition task and target detection task, participants in the multiple-talker condition showed evidence of facilitated statistical learning, particularly when listening to a novel talker. In the familiarity rating task, performance was comparable between conditions; however, participants trained with multiple talkers were less likely to conflate word familiarity with talker voice familiarity. Overall, these results suggest that training with multiple talkers can improve aspects of statistical learning across multiple measures of learning.
The present study aims at comparing the effects of two subtypes of cognitive reappraisal (i.e., stimulus-focused vs. goal-based reappraisal) to reduce anticipatory anxiety of pain. Affective ratings, startle reflex, and autonomic measures (electrodermal and heart rate changes) were used as a measure of emotion regulation success. A total of 86 undergraduate students completed an anticipatory task in which they had to regulate their negative emotions or react naturally when faced with the possibility of receiving a painful thermal stimulus. Participants were randomly assigned to two experimental groups to compare the stimulus-focused and goal-based strategies explored here. Our results revealed enhanced self-reported anxiety, electrodermal activity and eyeblink response when participants tried to voluntarily down-regulate their negative emotions, compared to the control instruction. Differences between both cognitive reappraisal groups were not found. These unexpected findings suggest that brief reappraisal instructions may not necessarily be favorable for regulating emotions during anticipation of aversive events. Moreover, these results are further explained in terms of the pain expectation, the painful stimuli modality, and emotion regulation instructions.
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)
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.
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.
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”.
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 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.
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.
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.
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.
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.
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.
This essay builds on previous studies of the “translanguaging” practices of members of the Montreal Hip Hop community (Low & Sarkar, 2014; Sarkar & Low, 2012) to explore how the challenge to linguistic norms in Quebec is also a challenge to social and identity norms. Our analysis is based on interviews with young musicians attending a community recording studio program in Montreal, the studio’s social media presence, and a track created in collaboration with youth in Paris. Through their relationships in the studio as well as their music, the youth de- and reterritorialize their identities (Papastergiadis, 2000), mobilizing local and global Hip Hop cultural references and communities in ways that challenge dominant narratives of belonging in Quebec and Canada.
The experience of stress is related to individual wellbeing and vulnerability to psychopathology. Therefore, understanding the determinants of individual differences in stress reactivity is of great concern from a clinical perspective. The functional promotor polymorphism of the serotonin transporter gene (5-HTTLPR/rs25531) is such a factor, which has been linked to the acute stress response as well as the adverse effect of life stressors. In the present study, we compared the impact of two different stress induction protocols (Maastricht Acute Stress Test and ScanSTRESS) and the respective control conditions on affective ratings, salivary cortisol levels and cognitive performance. To this end, 156 healthy young males were tested and genotyped for the 5-HTTLPR/rs25531 polymorphism. While combined physiological and psychological stress in the MAST led to a greater cortisol increase compared to control conditions as well as the psychosocial ScanSTRESS, subjective stress ratings were highest in the ScanSTRESS condition. Stress induction in general affected working memory capacity but not response inhibition. Subjective stress was also influenced by 5-HTTLPR/rs25531 genotype with the high expression group showing lower stress ratings than lower expression groups. In line with previous research, we identified the low expression variant of the serotonin transporter gene as a risk factor for increased stress reactivity. While some dimensions of the human stress response may be stressor specific, cognitive outcomes such as working memory performance are influenced by stress in general. Different pathways of stress processing and possible underlying mechanisms are discussed.
Clefts are understood as biclausal structures involving the movement of a clefted constituent from a lower clause, where it is generated, to a higher clause, where it is interpreted. Though both grammatical, subject and object clefts show signs of different acceptability in experimental settings. This degradation is ascribed to the fact that the object needs to cross an intervening subject, thus triggering intervention effects. In this paper, we show that intervention effects are also present in grammatical configurations, and give rise to lower-than-expected frequencies. Based on sets of features that play a role in the syntactic computation of locality, we compare the theoretically expected and the actually observed counts of features in a corpus of thirteen syntactically annotated treebanks for three languages (English, French, Italian). We find the quantitative effects predicted by the theory of intervention locality: object clefts are less frequent than expected in intervention configuration, while subject clefts are roughly as frequent as expected. We also find that the size of the effect is proportional to the number of features that give rise to the intervention effect. These results provide a three-fold contribution. First, they extend the empirical evidence in favour of the feature-based intervention theory of locality. Second, they provide theory-driven quantitative evidence, thus extending in a novel way the sources of evidence used to adjudicate theories. Finally, the paper provides a blueprint for future theory-driven quantitative investigations.
Initial results of neural architecture search (NAS) in natural language processing (NLP) have been achieved, but the search space of most NAS methods is based on the simplest recurrent cell and thus does not consider the modeling of long sequences. The remote information tends to disappear gradually when the input sequence is long, resulting in poor model performance. In this paper, we present an approach based on dual cells to search for a better-performing network architecture. We construct a search space that is more compatible with language modeling tasks by adding an information storage cell inside the search cell, so that we can make better use of the remote information of the sequence and improve the performance of the model. The language model searched by our method achieves better results than those of the baseline method on the Penn Treebank data set and WikiText-2 data set.
This study examined involuntary capture of attention, overt attention, and stimulus valence and arousal ratings, all factors that can contribute to potential attentional biases to face and train objects in children with and without autism spectrum disorder (ASD). In the visual domain, faces are particularly captivating, and are thought to have a 'special status' in the attentional system. Research suggests that similar attentional biases may exist for other objects of expertise (e.g. birds for bird experts), providing support for the role of exposure in attention prioritization. Autistic individuals often have circumscribed interests around certain classes of objects, such as trains, that are related to vehicles and mechanical systems. This research aimed to determine whether this propensity in autistic individuals leads to stronger attention capture by trains, and perhaps weaker attention capture by faces, than what would be expected in non-autistic children. In Experiment 1, autistic children (6-14 years old) and age- and IQ-matched non-autistic children performed a visual search task where they manually indicated whether a target butterfly appeared amongst an array of face, train, and neutral distractors while their eye-movements were tracked. Autistic children were no less susceptible to attention capture by faces than non-autistic children. Overall, for both groups, trains captured attention more strongly than face stimuli and, trains had a larger effect on overt attention to the target stimuli, relative to face distractors. In Experiment 2, a new group of children (autistic and non-autistic) rated train stimuli as more interesting and exciting than the face stimuli, with no differences between groups. These results suggest that: (1) other objects (trains) can capture attention in a similar manner as faces, in both autistic and non-autistic children (2) attention capture is driven partly by voluntary attentional processes related to personal interest or affective responses to the stimuli.
In this paper, we leverage pre-trained language models (PLMs) to precisely evaluate the semantics preservation of edition process on sentences. Our metric, Neighbor Distribution Divergence (NDD), evaluates the disturbance on predicted distribution of neighboring words from mask language model (MLM). NDD is capable of detecting precise changes in semantics which are easily ignored by text similarity. By exploiting the property of NDD, we implement a unsupervised and even training-free algorithm for extractive sentence compression. We show that our NDD-based algorithm outperforms previous perplexity-based unsupervised algorithm by a large margin. For further exploration on interpretability, we evaluate NDD by pruning on syntactic dependency treebanks and apply NDD for predicate detection as well.
Can the presence of unrelated flanker words change the way that lexical decisions are made to target words in the flankers task? Here we examined the impact of flanker presence on the effects of word concreteness. Target words had high or low concreteness ratings (e.g., fork, free) and were either presented in isolation or flanked to the left and right by an unrelated word (e.g., cold free cold) that was irrelevant for the task. Results revealed that the facilitatory effect of concreteness (faster responses to concrete words compared with abstract words) was significantly greater in the presence of flankers. A control experiment revealed the same pattern with pseudoword and nonword flankers. We conclude that the mere presence of flanking letter strings causes a greater depth of processing of target words. We further speculate that this might arise by flankers inducing a more "sentence-like" context by the presence of multiple, spatially distinct letter strings, that prohibits the use of more superficial decision processes and can be used to make lexical decisions to isolated words.
Research has identified three different types of smiles – the reward, affiliation and dominance smile – which serve expressions of happiness, connectedness, and superiority, respectively. Examining their explicit and implicit evaluations by considering a perceivers’ level of social anxiety and psychopathy may enhance our understanding of these smiles’ theorised meanings, and their role in problematic social behaviour. Female participants (N=122) filled in questionnaires on social anxiety, psychopathic tendencies (i.e. the affective-interpersonal deficit and antisocial lifestyle) and callous–unemotional (CU) traits. In order to measure explicit and implicit evaluations of the three smiles, angry and neutral facial expressions, an Explicit Valence Rating Task and an Approach-Avoidance Task were administered. Results indicated that all smiles were explicitly evaluated as positive. No differences in implicit evaluations between the smile types were found. Social anxiety was not associated with either explicit or implicit smile evaluations. In contrast, CU-traits were negatively associated with explicit evaluations of reward and dominance smiles. These findings support the assumptions of non-biased explicit information processing in social anxiety, and flattened emotional sensitivity in CU-traits. The importance of a multimethod approach to enhance the understanding of the effects of smile types on perceivers is discussed.
The presence of a partner can attenuate physiological fear responses, a phenomenon known as social buffering. However, not all individuals are equally sociable. Here we investigated whether social buffering of fear is shaped by sensitivity to social anxiety (social concern) and whether these effects are different in females and males. We collected skin conductance responses (SCRs) and affect ratings of female and male participants when they experienced aversive and neutral sounds alone (alone treatment) or in the presence of an unknown person of the same gender (social treatment). Individual differences in social concern were assessed based on a well-established questionnaire. Our results showed that social concern had a stronger effect on social buffering in females than in males. The lower females scored on social concern, the stronger the SCRs reduction in the social compared to the alone treatment. The effect of social concern on social buffering of fear in females disappeared if participants were paired with a virtual agent instead of a real person. Together, these results showed that social buffering of human fear is shaped by gender and social concern. In females, the presence of virtual agents can buffer fear, irrespective of individual differences in social concern. These findings specify factors that shape the social modulation of human fear, and thus might be relevant for the treatment of anxiety disorders.
FrameNet is a lexical semantic resource based on the linguistic theory of frame semantics. A number of framenet development strategies have been reported previously and all of them involve exploration of corpora and a fair amount of manual work. Despite previous efforts, there does not exist a well-thought-out automatic/semi-automatic methodology for frame construction. In this paper we propose a data-driven methodology for identification and semi-automatic construction of frames. As a proof of concept, we report on our initial attempts to build a widerscale framenet for the legal domain (LawFN) using the proposed methodology. The constructed frames are stored in a lexical database and together with the annotated example sentences they have been made available through a web interface.
Article Hanne Martine Eckhoff, Silvia Luraghi u. Marco Passarotti (Hgg.): Diachronic Treebanks for Historical Linguistics, Amsterdam u. Philadelphia: John Benjamins 2020, 154 S. (Benjamins Current Topics 113) was published on May 1, 2021 in the journal Beiträge zur Geschichte der deutschen Sprache und Literatur (volume 143, issue 2).
Implicit and explicit attitudes influence our behavior. Accordingly, it was the main goal of the paper to investigate if those attitudes are related to body image satisfaction. 134 young women between 18 and 34 years completed an explicit affective rating and an implicit affective priming task with pictures of women with different BMIs. Because it is well known that mindfulness, self-compassion and social media activity influence body image satisfaction, these variables were registered as well. The results confirmed an explicit positive affective bias toward pictures of slim women and a negative bias toward emaciated and obese body pictures. It adds to the literature that the explicit positive bias does not hold true for the strongest form of underweight, suggesting that instead of dividing different body shapes into two groups, different gradings of under- and overweight should be considered. Concerning the affective priming task, no significant differences between the different pictures could be carved out. Implicit and explicit affective attitudes were not related to the body satisfaction of the participating women. In line with former studies, body satisfaction was predicted by the actual-ideal weight discrepancy, the BMI, aspects of mindfulness and self-compassion. This study indicates that implicit and explicit affective attitudes toward underweight and overweight women are unrelated to the participants' body satisfaction.
BACKGROUND: The affective states most strongly associated with nonsuicidal self-injury (NSSI) remain poorly understood, particularly among veterans. This study used ecological momentary assessment (EMA) to examine relationships between affect ratings and NSSI urges and behaviors among veterans with NSSI disorder. METHODS: Participants (N = 40) completed EMA entries via mobile phone for 28 days (3722 total entries). Entries included intensity ratings for five basic affective states, as well as NSSI urges and behaviors, during the past 4 hours. RESULTS: Bivariate analyses indicated that each affect variable was significantly associated with both NSSI urges and behaviors. Angry/hostile and sad were most strongly associated with both NSSI urges and behaviors. A multivariate regression revealed that angry/hostile, disgusted with self, and happy (inversely related) were contemporaneously (within the same period) associated with NSSI behaviors, whereas all five basic affective states were contemporaneously associated with NSSI urges. In a lagged model, angry/hostile and sad were associated with subsequent NSSI urges but not behaviors. CONCLUSIONS: Findings highlight the relevance of particular affective states to NSSI and the potential utility of targeting anger in treatments for NSSI among veterans. There is a need for future EMA research study to further investigate temporal relationships between these variables.
This paper investigates updates of Universal Dependencies (UD) treebanks in 23 languages and their impact on a downstream application. Numerous people are involved in updating UD's annotation guidelines and treebanks in various languages. However, it is not easy to verify whether the updated resources maintain universality with other language resources. Thus, validity and consistency of multilingual corpora should be tested through application tasks involving syntactic structures with PoS tags, dependency labels, and universal features. We apply the syntactic parsers trained on UD treebanks from multiple versions (2.0 to 2.7) to a clause-level sentiment extractor. We then analyze the relationships between attachment scores of dependency parsers and performance in application tasks. For future UD developments, we show examples of outputs that differ depending on version.
The paper investigates „Sprachliche Verrohung“ (linguistic neglection/brutalization), a term that has been recently and often used within the German mass media. It seems, however, that there is no common understanding of what „Sprachliche Verrohung“ is. To obtain a definition, a thinkaloud study was conducted: 40 participants judged relevant linguistic examples by verbalizing aloud their thoughts and ideas. The obtained think-aloud protocols are analysed and the following definition is derived: Expressions of „Sprachliche Verrohung“ are in conflict with the linguistic norm and speakers uses them despite their knowledge of this conflict.
Abstract In this paper we introduce an extended version of the Vedic Treebank ( vtb, Hellwig et al. 2020) which comes along with revisited and extended annotation guidelines. In order to assess the quality of our annotations as well as the usability and limits of the guidelines we performed an inter-annotator agreement test. The results show that agreement between annotators is hampered by various factors, most prominently by insufficient understanding of the content because of the cultural and temporal gap and incomplete knowledge of Vedic grammar. An in-depth discussion of disagreeing annotations demonstrates that the setup of the workflow, too, has a major influence on inter-annotator agreement. We suggest some measures that can help increase the transparency and annotation consistency according to current knowledge of the language when annotating Vedic Sanskrit, or ancient language varieties in general.
Abstract Of all the semantic domains, colour terms have attracted the largest amount of attention, notably from a typological point of view. However, there is much more to be discovered. A search of the cross-linguistic lexical database of African languages (RefLex) reveals several previously undetected areal colexification patterns and shared lexico-constructional patterns in a genetically balanced sample of 401 languages. In this paper, we illustrate several areal characteristics of colour terms: (i) the spread of an areal feature due to a common extra-linguistic setting (locust bean – Parkia biglobosa – as the lexical source of yellow ); (ii) two convergence phenomena, one based on a shared lexico-constructional pattern including a term for water, and one based on shared colexifications ( red and ripe vs. green and unripe ); and (iii) an areal pattern of lexical diffusion of colour ideophones, a category which has thus far been considered difficult to borrow.
The international development and social impact evidence community is divided about the use of machine-centered approaches in carrying out systematic reviews and maps. While some researchers argue that machine-centered approaches such as machine learning, artificial intelligence, text mining, automated semantic analysis, and translation bots are superior to human-centered ones, others claim the opposite. We argue that a hybrid approach combining machine and human-centered elements can have higher effectiveness, efficiency, and societal relevance than either approach can achieve alone. We present how combining lexical databases with dictionaries from crowdsourced literature, using full texts instead of titles, abstracts, and keywords. Using metadata sets can significantly improve the current practices of systematic reviews and maps. Since the use of machine-centered approaches in forestry and forestry-related reviews and maps are rare, the gains in effectiveness, efficiency, and relevance can be very high for the evidence base in forestry. We also argue that the benefits from our hybrid approach will increase in time as digital literacy and better ontologies improve globally.