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16504 papers
Child-directed speech, as a specialized form of speech directed toward young children, has been found across numerous languages around the world and has been suggested as a universal feature of human experience. However, variation in its implementation and the extent to which it is culturally supported has called its universality into question. Child-directed speech has also been posited to be associated with expression of positive affect or "happy talk." Here, we examined Canadian English-speaking adults' ability to discriminate child-directed from adult-directed speech samples from two dissimilar language/cultural communities; an urban Farsi-speaking population, and a rural, horticulturalist Tseltal Mayan speaking community. We also examined the relationship between participants' addressee classification and ratings of positive affect. Naive raters could successfully classify CDS in Farsi, but only trained raters were successful with the Tseltal Mayan sample. Associations with some affective ratings were found for the Farsi samples, but not reliably for happy speech. These findings point to a complex relationship between perception of affect and CDS, and context-specific effects on the ability to classify CDS across languages.
Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction. Although coordination has been explored extensively in the linguistics literature, the rules and constraints that govern its structure are still largely elusive and widely debated amongst linguists. This paper presents a study of two-termed unlike coordinations in particular, where the two conjuncts of the coordination phrase form valid constituents but have distinct categories. We conducted a syntactic analysis of the phrasal categories that can be conjoined in such unlike coordinations through a computational corpusbased approach, utilizing the Corpus of Contemporary American English (COCA) as the main data source, as well as the Penn Treebank (PTB). The results show that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination. This research provides new data and perspectives through the use of statistical techniques that can help shape future theories and models of coordination.
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.
How health-related messages are framed can impact their effectiveness in promoting behaviors, and messages framed in terms of gains have been shown to be more effective among older adults. Recent findings have suggested that the affective response to framed messages can contribute to these effects. However, the impact of demands associated with psycholinguistic processing for different frames is not well understood. In this study, exercise-related messages were gain or loss framed and with a focus on either desirable or undesirable outcomes. Participants read these messages while their eye movements were monitored and then provided affective ratings. Older adults reacted less negatively than younger adults to loss-framed messages and messages focusing on undesirable outcomes. Eye-movement measures indicated both younger and older adults had difficulty processing the most complex messages (loss-framed messages focused on avoiding desirable outcomes). When gain-framed messages were easily processed, they engendered more positive affect, which in turn, was related to better recall. These results suggest that affective and cognitive mechanisms are interdependent in comprehension of framed messages for younger and older adults. An implication for translation to effective health communication is that simpler message framing engenders a positive reaction, which in turn supports memory for that information, regardless of age. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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.
With a pair of oppositely valenced stimuli, rating the first one sometimes leads to a more extreme evaluation for the second (e.g., if the second is negatively valenced, rating the first stimulus would lead to a more negative rating for the second). We considered an evaluation bias in the case of clinical diagnosis relating to eating disorders. A population sample which included experienced clinical psychologists and psychiatrists showed partial evidence of an evaluation bias, when judging descriptions of individuals designed to be consistent with eating disorders or not. Quantum probability theory, the probability rules from quantum mechanics without any of the physics, is particularly well-suited to modeling the evaluation bias (and constructive influences generally), because a measurement (or judgment) can change the state of the system. We applied a previous quantum model to the present result, an extension of the model embodying noisy processes, and belief adjustment model. We discuss how model fits inform an examination of rationality in the observed behavior.
The categorization of dominant facial features, such as sex, is a highly relevant function for social interaction. It has been found that attributes of the perceiver, such as their biological sex, influence the perception of sexually dimorphic facial features with women showing higher recognition performance for female faces than men. However, evidence on how aspects closely related to biological sex influence face sex categorization are scarce. Using a previously validated set of sex-morphed facial images (morphed from male to female and vice versa), we aimed to investigate the influence of the participant’s gender role identification and sexual orientation on face sex categorization, besides their biological sex. Image ratings, questionnaire data on gender role identification and sexual orientation were collected from 67 adults (34 females). Contrary to previous literature, biological sex per se was not significantly associated with image ratings. However, an influence of participant sexual attraction and gender role identity became apparent: participants identifying with male gender attributes and showing attraction toward females perceived masculinized female faces as more male and femininized male faces as more female when compared to participants identifying with female gender attributes and attraction toward males. Considering that we found these effects in a predominantly cisgender and heterosexual sample, investigation of face sex perception in individuals identifying with a gender different from their assigned sex (i.e., transgender people) might provide further insights into how assigned sex and gender identity are related.
Abstract The lexicon of emotion words is fundamental to interpersonal communication. To examine how emotion word acquisition interacts with societal context, the present study investigated emotion word development in three groups of child Korean users aged 4–13 years: those who use Korean primarily outside the home as a majority language (MajKCs) or inside the home as a minority language (MinKCs), and those who use Korean both inside and outside the home (KCs). These groups, along with a group of L1 Korean adults, rated the emotional valence of 61 Korean emotion words varying in frequency, valence, and age of acquisition. Results showed KCs, MajKCs, and MinKCs all converging toward adult-like valence ratings by ages 11–13 years; unlike KCs and MajKCs, however, MinKCs did not show age-graded development and continued to diverge from adults in emotion word knowledge by these later ages. These findings support the view that societal context plays a major role in emotion word development, offering one reason for the intergenerational communication difficulties reported by immigrant families.
From a simplified analysis of adaptive methods, we derive AvaGrad, a new optimizer which outperforms SGD on vision tasks when its adaptability is properly tuned. We observe that the power of our method is partially explained by a decoupling of learning rate and adaptability, greatly simplifying hyperparameter search. In light of this observation, we demonstrate that, against conventional wisdom, Adam can also outperform SGD on vision tasks, as long as the coupling between its learning rate and adaptability is taken into account. In practice, AvaGrad matches the best results, as measured by generalization accuracy, delivered by any existing optimizer (SGD or adaptive) across image classification (CIFAR, ImageNet) and character-level language modelling (Penn Treebank) tasks. When training GANs, AvaGrad improves upon existing optimizers. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Background: Limited research has investigated whether replacing psychiatric diagnosis with psychological formulation-based approaches has implications for lay attitudes to mental health. The present study investigates experimentally whether presenting psychosis in terms of a schizophrenia diagnosis vs. formulation narrative affects stigma and treatment attitudes in the general public.Method: The study employed a between-groups experimental vignette design, with data collected online. 351 participants (64.1% female, aged 18–66, ) read a vignette about a person experiencing psychosis, defined with either a diagnosis of schizophrenia or a narrative-based formulation. Participants completed a battery of scales measuring their attitudes to the vignette character (social distance, attribution, recommended treatment options, mental help-seeking attitudes).Results: Desired social distance was significantly greater in participants exposed to the diagnostic label of schizophrenia. The schizophrenia label led participants to rate medical care as significantly more helpful relative to the formulation condition but did not affect ratings of specialist or community care or mental help-seeking attitudes.Conclusions: These findings suggest that a psychological formulation approach may slightly lessen stigma-related attitudes, relative to traditional diagnostic systems. Popularisation of formulation models need not compromise general orientations to help-seeking or perceived helpfulness of specialist care but may lead to less medicalised treatment preferences.
Accurate recovery of predicate-argument structure from a Universal Dependency (UD) parse is central to downstream tasks such as extraction of semantic roles or event representations. This study introduces compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse. Accuracy of compchain classification serves as a proxy for measuring accurate recovery of predicate-argument structure from sentences with embedding. We analyzed the distribution of compchains in three UD English treebanks, EWT, GUM and LinES, revealing that these treebanks are sparse with respect to sentences with predicate-argument structure that includes predicate-argument embedding. We evaluated the CoNLL 2018 Shared Task UDPipe (v1.2) baseline (dependency parsing) models as compchain classifiers for the EWT, GUMS and LinES UD treebanks. Our results indicate that these three baseline models exhibit poorer performance on sentences with predicate-argument structure with more than one level of embedding; we used compchains to characterize the errors made by these parsers and present examples of erroneous parses produced by the parser that were identified using compchains. We also analyzed the distribution of compchains in 58 non-English UD treebanks and then used compchains to evaluate the CoNLL'18 Shared Task baseline model for each of these treebanks. Our analysis shows that performance with respect to compchain classification is only weakly correlated with the official evaluation metrics (LAS, MLAS and BLEX). We identify gaps in the distribution of compchains in several of the UD treebanks, thus providing a roadmap for how these treebanks may be supplemented. We conclude by discussing how compchains provide a new perspective on the sparsity of training data for UD parsers, as well as the accuracy of the resulting UD parses.
Affective experiences occur across the wake-sleep cycle-from active wakefulness to resting wakefulness (i.e., mind-wandering) to sleep (i.e., dreaming). Yet, we know little about the dynamics of affect across these states. We compared the affective ratings of waking, mind-wandering, and dream episodes. Results showed that mind-wandering was more positively valenced than dreaming, and that both mind-wandering and dreaming were more negatively valenced than active wakefulness. We also compared participants' self-ratings of affect with external ratings of affect (i.e., analysis of affect in verbal reports). With self-ratings all episodes were predominated by positive affect. However, the affective valence of reports changed from positively valenced waking reports to affectively balanced mind-wandering reports to negatively valenced dream reports. These findings show that (1) the positivity bias characteristic to waking experiences decreases across the wake-sleep continuum, and (2) conclusions regarding affective experiences depend on whether self-ratings or verbal reports describing these experiences are analysed.
Reproduction of knowledge, especially tacit knowledge can be expensive during a pandemic. One of the most common causes is the reduced information accessibility during the translation process. Having the ability to assess the linguistic complexity of any given contents could potentially improve knowledge reproduction. Authors conduct two cross-linguistic studies on the World Health Organization (WHO)'s emergency learning platform to assess the linguistic complexity of two online courses in 10 languages. Morpho-syntactically annotated treebanks, unannotated materials from Wikipedia and language-specific corpora are set as control groups. Preliminary findings reveal a clear reduced complexity of learning contents in the most candidate languages while retaining the maximum amount of information. Creating a baseline study on low-resourced languages on the learning genre could be potentially useful for measuring impact of normative products at country and local level.
Sparse neural networks have been widely applied to reduce the computational demands of training and deploying over-parameterized deep neural networks. For inference acceleration, methods that discover a sparse network from a pre-trained dense network (dense-to-sparse training) work effectively. Recently, dynamic sparse training (DST) has been proposed to train sparse neural networks without pre-training a dense model (sparse-to-sparse training), 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 the Recurrent Neural Networks (RNNs) setting. In this paper, we propose an approach to train intrinsically 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 better regularization. Further, we propose SNT-ASGD, a novel 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, better than the dense-to-sparse methods, with various types of RNNs on Penn TreeBank and Wikitext-2 datasets. Our codes are available at https://github.com/Shiweiliuiiiiiii/Selfish-RNN.
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 highresource languages. Building language models and, more generally, NLP systems for nonstandardized and low-resource languages remains a challenging task. In this work, we focus 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 displaying 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 characterbased 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 pretrained 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.
This article provides information on the issue of naming in world marketing, naming technology various problems related to the linguistic aspect of naming technology, the specific norms of name formation in the Uzbek language. The sign of informativeness of the names of trading objects also indicates the original purpose of this object, what products it is intended to trade with. In creating a name, it is necessary to take into account the linguistic norms of a particular language, as well as people’s culture, worldview, mentality, psychology, etc. The name created by it serves as a useful communicative communication function between the commercial object and the consumer, the name helps the commercial object to occupy a strong position in the market competition. From this point of view, the development of norms for naming specific objects of each language is one of the urgent tasks of today.
Emotion processing abnormalities and sleep pathology are central to the phenomenology of paediatric posttraumatic stress disorder, and sleep disturbance has been linked to the development, maintenance and severity of the disorder. Given emerging evidence indicating a role for sleep in emotional brain function, it has been proposed that dysfunctional processing of emotional experiences during sleep may play a significant role in affective disorders, including posttraumatic stress disorder. Here we sought to examine the relationship between sleep and emotion processing in typically developing youth, and youth with a diagnosis of posttraumatic stress disorder. We use high-density electroencephalogram to compare baseline sleep with sleep following performance on a task designed to assess both memory for and reactivity to negative and neutral imagery in 10 youths with posttraumatic stress disorder, and 10 age- and sex-matched non-traumatized typically developing youths. Subjective ratings of arousal to negative imagery (ΔArousal = post-sleep minus pre-sleep arousal ratings) remain unchanged in youth with posttraumatic stress disorder following sleep (mean increase 0.15, CI -0.28 to +0.58), but decreased in TD youth (mean decrease -1.0, 95% CI -1.44 to -0.58). ΔArousal, or affective habituation, was negatively correlated with global change in slow-wave activity power (ρ = -0.58, p =.008). When considered topographically, the correlation between Δslow-wave activity power and affective habituation was most significant in a frontal cluster of 27 electrodes (Spearman, ρ = -0.51, p =.021). Our results highlight the importance of slow-wave sleep for adaptive emotional processing in youth, and have implications for symptom persistence in paediatric posttraumatic stress disorder. Impairments in slow-wave activity may represent a modifiable risk factor in paediatric posttraumatic stress disorder.
Postpartum Depression (PPD) is the most common non-obstetric complications associated with childbearing, but currently has poor diagnostic regimes. Sensory symptoms of PPD are understudied, particularly with regard to the sense of olfaction. The present study addresses this research gap by assessing differences in olfactory abilities between 39 depressed mothers, who were within the perinatal period (i.e., during pregnancy and up to 1-year post pregnancy) and assessed with Edinburgh Postnatal Depression Scale, and their case-matched healthy volunteers. The assessments include two olfactory testing sessions conducted 4-weeks apart, each comprising a standard odour detection threshold test (i.e., Snap & Sniff Olfactory Test System), and intensity and valence ratings for 3 "pleasant" and 3 "unpleasant" odorants. The results revealed no difference between patients (M = 5.6; SE = 0.3) and control group (M = 5.7; SE = 0.4) in terms of olfactory detection threshold. However, the patients group perceived the 3 "unpleasant" odours as significantly less pleasant (p < 0.05), and 2 odorants (1 "pleasant" and 1 "unpleasant") as less intense. Additionally, these results did not appear to be significantly interacted with the individual's perinatal stage. The present study is the first to evaluate associations between olfactory function and PPD. Findings from the study suggest that, while PPD has little effect on the early stages of olfactory processing, these conditions may have stronger influence on higher-order olfactory perception, including both hedonic and intensity perception. These novel findings add knowledge to sensory symptoms of PPD.
The problems inherent in efforts to create large lexical databases by mapping machine-readable dictionaries onto each other are illustrated through an attempt to merge manually two short entries (whistle and whistler) from two monolingual English collegiate-style dictionaries. In view of the complexities revealed by this operation for the human mind, let alone the machine, we suggest that if lexical databases are to be created by automatic or semi-automatic means, it is essential to design the complete database first, following the demands of the language as identified by theoretical linguistic research, and only then to attempt to map the contents of any machine-readable dictionary into this ‘ideal’ database. We exemplify part of the suggested template lexical entry for verbs of sound, including whistle. 1
Approach biases to foods may explain why food consumption often diverges from deliberate dietary intentions. Yet, the assessment of behavioural biases with the approach-avoidance tasks (AAT) is often unreliable and validity is partially unclear. The present study continues a series of studies that develop a task based on naturalistic approach and avoidance movements on a touchscreen (hand-AAT). In the hand-AAT, participants are instructed to respond based on the food/non-food distinction, thereby ensuring attention to the stimuli. Yet, this implies the use of instruction switches (i.e., 'approach food - avoid objects' to 'avoid food - approach objects'), which introduce order effects. The present study increased the number of instruction switches to potentially minimize order effects, and re-examined reliability. We additionally included the implicit association task (IAT) and several self-reported eating behaviours to investigate the task's validity. Results replicated the presence of reliable approach biases to foods irrespective of instruction order. Evidence for validity, however, was mixed: biases correlated positively with external eating, increase in food craving and aggregated image valence ratings but not with desire to eat ratings of the individual images considered within participants or the IAT. We conclude that the hand-AAT can reliably assess approach biases to foods that are relevant to self-reported eating patterns.
Human uses social media platform such as Twitter to express feelings and opinions through text about the surrounding issues. Understanding emotions at the subtle level of expressed feelings are essential for better human and computer interactions. The previous emotion recognition approach required many training data and lexical databases. Unfortunately, the availability of very little labeled training data is a limitation and challenge to achieving high model performance. Therefore, we investigate the BERT language model for emotion recognition in Indonesian-language Tweets in this study. We choose to use fine-tuning instead of pre-training, which requires extensive data and resources. Two pre-trained models were used to determine the effectiveness and performance of the proposed model. Experiments show that the proposed model outperforms all existing baseline models, with the highest accuracy is 77%. Another advantage that we analyze is that BERT requires a relatively short computation time. In addition, BERT has a better context representation.
In this article, we provide preliminary evidence for the 'hypersensitivity hypothesis', according to which Emotional Intelligence (EI) functions as a magnifier of emotional experience, enhancing the effect of emotion and emotion information on thinking and social perception. Measuring ability EI, and in particular Emotion Understanding, we describe an experiment designed to determine whether, relative to those low in EI, individuals high in EI were more affected by the valence of a scenario describing a target when making an affective social judgment. Employing a sample of individuals from the general population, high EI participants were found to provide more extreme (positive or negative) impressions of the target as a function of the scenario valence: positive information about the target increased high EI participants' positive impressions more than it increased low EI participants' impressions, and negative information increased their negative impressions more. In addition, EI affected the amount of recalled information and this led high EI individuals to intensify their affective ratings of the target. These initial results show that individuals high on EI may be particularly sensitive to emotions and emotion information, and they suggest that this hypersensitivity might account for both the beneficial and detrimental effects of EI documented in the literature. Implications are discussed.
The study was designed to test the hypothesis that indirect inhibition of the insula via cathodal transcranial direct current stimulation (tDCS) would decrease disgust and moral rigidity in 36 healthy individuals undergoing 15 min of tDCS over the temporal lobe. To obtain a comprehensive assessment of disgust, we used subjective (affect rating), physiological (heart rate variability [HRV]), and implicit measures (word-fragment completion), and moral judgment was assessed by asking participants to rate the deontological and altruistic moral wrongness of a revised version of the moral foundations vignettes. We found anodal and cathodal stimulations to, respectively, enhance and decrease self-reported disgust, deontological morality, and HRV. Note that these effects were stronger in individuals with higher levels of obsessive compulsive (OC) traits. Because disgust and sensitivity to deontological guilt are among the most impairing features in OC disorder, it is auspicious that cathodal tDCS could be implemented to reduce such symptoms.
Emotion recognition ability (ERA) predicts more successful interpersonal interactions. However, it remains unknown whether ERA training can affect behaviors and improve social outcomes in such interactions. Here, 83 dyads of same-gender students completed either a self-administered 45 min ERA training based on audio-visual clips of 14 different emotions, or a control training about cloud types. All dyads then engaged in a face-to-face employee-recruiter negotiation about a job contract. Dyads trained in ERA reached more egalitarian economic outcomes, rated themselves and their partners as less competitive after the negotiation, and received more positive affect ratings as well as lower ratings on forcing from independent observers. Applications of the training in the context of work, education, and therapy are discussed.
Timbre is one of the psychophysical cues that has a great impact on affect perception, although, it has not been the subject of much cross-cultural research. Our aim is to investigate the influence of timbre on the perception of affect conveyed by Western and Chinese classical music using a cross-cultural approach. Four listener groups (Western musicians, Western nonmusicians, Chinese musicians, and Chinese nonmusicians; 40 per group) were presented with 48 musical excerpts, which included two musical excerpts (one piece of Chinese and one piece of Western classical music) per affect quadrant from the valence-arousal space, representing angry, happy, peaceful, and sad emotions and played with six different instruments ( erhu, dizi, pipa, violin, flute, and guitar). Participants reported ratings of valence, tension arousal, energy arousal, preference, and familiarity on continuous scales ranging from 1 to 9. ANOVA reveals that participants’ cultural backgrounds have a greater impact on affect perception than their musical backgrounds, and musicians more clearly distinguish between a perceived measure (valence) and a felt measure (preference) than do nonmusicians. We applied linear partial least squares regression to explore the relation between affect perception and acoustic features. The results show that the important acoustic features for valence and energy arousal are similar, which are related mostly to spectral variation, the shape of the temporal envelope, and the dynamic range. The important acoustic features for tension arousal describe the shape of the spectral envelope, noisiness, and the shape of the temporal envelope. The explanation for the similarity of perceived affect ratings between instruments is the similar acoustic features that were caused by the physical characteristics of specific instruments and performing techniques.
Part-of-Speech (POS) tagging is a fundamental sequence labeling problem in Natural Language Processing. Recent deep learning sequential models combine the forward and backward word informatio for POS tagging. The information of contextual words to the current word play a vital role in capturing the non-continuous relationship. We have proposed Monotonic chunk-wise attention with CNN-GRU-Softmax (MCCGS), a deep learning architecture that adheres to these essential information. This architecture consists of Input Encoder (IE), encodes word and character-level, Contextual Encoder (CE), assigns the weightage to adjacent word and Disambiguator (D), which resolves intra-label dependencies as core components. Moreover, different morphological features have been integrated into the core components of MCCGS architecture as MCCGS-IE, MCCGS-CE and MCCGS-D. The MCCGS architecture is validated on the 21 languages from Universal Dependency (UD) treebank. The state-of-the-art models, Type constraints, Retrofitting, Distant Supervision from Disparate Sources and Position-aware Self Attention, MCCGS and its variants such as MCCGS-IE, MCCGS-CE and MCCGS-D are obtained mean accuracy 83.65%, 81.29%, 84.10%, 90.18%, 90.40%, 91.40%, 90.90%, 92.30%, respectively. The proposed model architecture provides state-of-the-art accuracy on the low resource languages as Marathi (93.58%), Tamil (87.50%), Telugu (96.69%) and Sanskrit (97.28%) from UD treebank and Hindi (95.64%) and Urdu (87.47%) from Hindi-Urdu multi-representational treebank.
Recently, deep learning methods have greatly improved the state-of-the-art in many natural language processing tasks. Previous work shows that the Transformer can capture long-distance relations between words in a sequence. In this paper, we propose a Transformer-based neural model for Chinese word segmentation and part-ofspeech tagging. In the model, we present a word boundary-based character embedding method to overcome the character ambiguity problem. After the Transformer layer, BiLSTM-CRF layer is used to generate the best tagging results. Experiments on Chinese Treebank show that our model on Chinese word segmentation and part-of-speech tagging outperforms the baseline model and achieves state-of-the-art performance.
The Menzerath law is considered to show an aspect of the complexity underlying natural language. This law suggests that, for a linguistic unit, the size (y) of a linguistic construct decreases as the number (x) of constructs in the unit increases. This article investigates this property syntactically, with x as the number of constituents modifying the main predicate of a sentence and y as the size of those constituents in terms of the number of words. Following previous articles that demonstrated that the Menzerath property held for dependency corpora, such as in Czech and Ukrainian, this article first examines how well the property applies across languages by using the entire Universal Dependency dataset ver. 2.3, including 76 languages over 129 corpora and the Penn Treebank (PTB). The results show that the law holds reasonably well for x>2. Then, for comparison, the property is investigated with syntactically randomized sentences generated from the PTB. These results show that the property is almost reproducible even from simple random data. Further analysis of the property highlights more detailed characteristics of natural language.
The analytic hierarchy process (AHP) is a well-known approach in decision-making because of its simplicity and rationality. However, in conventional AHP, it cannot account for the correlation effect between criteria. In this paper, we use the lexical database, WordNet, to calculate the similarity between criteria set by a decision-maker. Then, we use the similarity matrix to process the factor analysis and obtain the independent factors, which are composed of their criteria. Finally, the weights of factors are derived to evaluate the alternatives. Moreover, we use a case study of online shopping to illustrate the proposed method and compare the result with the conventional AHP.
Recurrent Neural Network (RNN) is a widely used deep learning architecture applied to sequence learning problems. However, it is recognized that RNNs suffer from exploding and vanishing gradient problems that prohibit the early layers of the network from learning the gradient information. GRU networks are particular kinds of recurrent networks that reduce the short-comings of these problems. In this study, we propose two variants of the standard GRU with simple but effective modifications. We applied an empirical approach and tried to determine the effectiveness of the current units and recurrent units of gates by giving different coefficients. Interestingly, we realize that applying such minor and simple changes to the standard GRU provides notable improvements. We comparatively evaluate the standard GRU with the proposed two variants on four different tasks: (1) sentiment classification on the IMDB movie review dataset, (2) language modeling task on Penn TreeBank (PTB) dataset, (3) sequence to sequence addition problem, and (4) question answering problem on Facebook’s bAbitasks dataset. The evaluation results indicate that the proposed two variants of GRU consistently outperform standard GRU.
Each language has its own linguistic laws of name creation. Regardless of the field in which Naming technology works as a type of activity, its main goal in linguistics is to develop linguistic norms for the creation of a specific language-specific name. Every name, developed in accordance with linguistic norms, should help trade, production facilities, products to be competitive in the market, to develop, gain fame and spread widely. This article analyzes a number of requirements and criteria for name creation in linguistics, developed by Neimer-practitioners, and analyzes the models of name creation in the Uzbek language.
The combined use of neural scoring systems and BERT fine-tuning has led to very high results in many natural language processing (NLP) tasks. These high results raise two important questions about the contribution and the limitations of pretrained-language models: (i) what are the remaining errors in the bestperforming systems? (ii) what are the types of test examples where pretrained language models help the most? In this paper, we investigate both questions for the task of English discontinuous constituency parsing on the Penn Treebank, for which recent models obtain close to 95 F 1 score. To do so, we propose two methods for automatically analysing the errors of discontinuous parser. First, we annotate and release a test-suite focused on the syntactic phenomena responsible for discontinuities in the Penn Treebank, enabling us to obtain a per-phenomenon evaluation of a parser's output. Second, we extend the Berkeley Parser Analyser -a tool that classifies parsing errors according to predefined structural patterns -, to discontinuous trees. We apply both methods to characterize errors of a state-of-theart transition-based discontinuous parser, and to provide an overview of the contribution of BERT to this task.
Sentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space.
Existing family language policy (FLP) scholarship has been criticised for insufficiently addressing children’s voices and perspectives on their multilingual experiences, as well as lacking representation and heterogeneity in terms of studies involving multilingual families from diverse family types, languages, and contexts outside the experiences of Western middle-class bilingual families. Against this backdrop, this paper examines the multilingual familial experiences of three Ethiopian and Eritrean migrant families in Sweden by paying particular attention to children’s agency and caregiver-children dynamics in FLP making. The study draws on multimodal biographic data obtained from children and parents through language portrait methods of body and space mapping activities, post-mapping narration, and semi-structured interviews. The data are analysed in light of Smith-Christmas’s (2020) framework, which views child agency in FLP at the intersection of compliance regime, linguistic competence, linguistic norms, and power dynamics. The findings reveal that the process of FLP making is characterised as a process that is (1) filled with language choice dilemmas triggered by competing linguistic demands, (2) in part shaped by the family constellation via power dynamics between family members, and (3) mediated by family members’ varied linguistic proficiencies in majority and minority languages. Additionally, children’s agency about which language they choose to use impacts the language practices of the home, as they tend to establish their own linguistic norms within the home by overrunning the negotiated language policy set by caregivers.
Music’s ability to influence exercise performance is well known, but the converse, how exercise influences music listening, remains largely unknown. Exercise can elevate arousal, positive affect, and neurotransmitters including dopamine, which are involved in musical pleasure. Here we examine how exercise influences music enjoyment, and test for a modulatory role of arousal, affect, and dopamine. Before and after exercise (12 min of vigorous running) and a rest control session, participants ( N = 20) listened to music clips and rated their enjoyment and subjective arousal; we also collected ratings of affect and eye-blink rates, an established predictor of dopamine activity. Ratings of musical enjoyment increased significantly after exercise, but not after the rest control condition. While changes in subjective arousal ratings did not differ between exercise and control conditions, change in subjective arousal correlated with change in music enjoyment. After exercise, the change in music enjoyment had a positive but non-significant correlation with change in eye-blink rates ( r =.36). Positive affect increased more after exercise than after the control session, but the change in positive affect did not correlate with change in music enjoyment. In sum, exercise leads to increased musical enjoyment, and this effect was related to changes in arousal.