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18265 papers
The paper studies the effect of emotional states modulated by auditory stimuli on the cognitive control on decision making. Based on other previous neuroimaging studies, functional near-infrared spectroscopy provided reliable neuroimaging measurement in analyzing emotional states by studying the changes of hemodynamic response in prefrontal cortex (PFC). This experiment involved 16 nursing students. During the experiment, participants were given one minute to complete five nursing practice questions with five sequential repetitions in the presence of neutral and negative emotional auditory stimuli in two separated sessions under fNIRS measurement. The sound stimuli was selected from the International Affective Digitized Sound (IADS) System. The neutral auditory stimuli had neutral valence and medium arousal rating whereas negative auditory stimuli had negative valence and high arousal rating. The data collected was preprocessed by using wavelet transform to decompose the data into different frequency intervals. By selecting the frequency interval of interest, we analyzed the data based on functional connectivity within prefrontal cortex regions. We computed the regional wavelet coherence values between affective and neutral tasks. From the behavioral analysis, we found that subjects had significantly higher accuracy in affective task compared to neutral task. Based on the analysis, we found that left prefrontal cortex produced significantly lower wavelet coherence value but the highest coherence-accuracy correlation in affective task than in neutral task.
Characterizing the distribution of crossing dependencies in natural language dependency trees is a crucial task for building parsers and understanding the formal properties of human language. A number of formal restrictions on crossing dependencies have been proposed, including bounds on gap degree, edge degree, and end-point crossings. Here we ask whether the empirical distribution of crossing dependencies in dependency treebanks offers evidence for these formal restrictions as true, independent constraints on dependency trees, or whether the distribution can be explained using other, more generic constraints affecting dependency trees. Specifically, we explore the null hypothesis that crossing dependencies are formally unrestricted, but occur at a low rate. We implement the null hypothesis using random trees where crossing dependencies occur at the same rate as in natural language trees, but without any formal restrictions. We find that this baseline generally does not reproduce the same distribution of gap degree, edge degree, endpoint-crossing, and heads' depth difference as real trees, suggesting that these formal constraints are a consequence of factors beyond the rate of crossing dependencies alone.
Mirror-sensory synaesthetes mirror the pain or touch that they observe in other people on their own bodies. This type of synaesthesia has been associated with enhanced empathy. We investigated whether the enhanced empathy of people with mirror-sensory synesthesia influences the experience of situations involving touch or pain and whether it affects their prosocial decision making. Mirror-sensory synaesthetes ( N = 18, all female), verified with a touch-interference paradigm, were compared with a similar number of age-matched control individuals (all female). Participants viewed arousing images depicting pain or touch; we recorded subjective valence and arousal ratings, and physiological responses, hypothesizing more extreme reactions in synaesthetes. The subjective impact of positive and negative images was stronger in synaesthetes than in control participants; the stronger the reported synaesthesia, the more extreme the picture ratings. However, there was no evidence for differential physiological or hormonal responses to arousing pictures. Prosocial decision making was assessed with an economic game assessing altruism, in which participants had to divide money between themselves and a second player. Mirror-sensory synaesthetes donated more money than non-synaesthetes, showing enhanced prosocial behaviour, and also scored higher on the Interpersonal Reactivity Index as a measure of empathy. Our study demonstrates the subjective impact of mirror-sensory synaesthesia and its stimulating influence on prosocial behaviour. This article is part of the discussion meeting issue ‘Bridging senses: novel insights from synaesthesia’.
Automatic emotion regulation (AER) is an important type of emotion regulation in our daily life. Most of the previous studies concerning AER are done in the conscious level. Little is known about the AER under the subliminal level. The present study was to investigate the AER at the different perceptual levels (i.e., explicitly and implicitly) simultaneously, and the associated neural differences using functional magnetic resonance imaging. Priming paradigm was adopted in which the inhibition or neutral words were used as primes and the negative picutres were used as targets. In the experiment, the duration time of priming words was manipulated at 33 or 50 ms in the implicit level and 3000 ms in the explicit level. The participants were required to make emotional valence rating of the negative pictures while undergoing functional magnetic resonance imaging scanning. The results showed that the participants experienced less negative emotion in inhibition words priming condition contrary to neutral words priming condition. Significant differences were also found in the left ventrolateral prefrontal cortex and left dorsolateral prefrontal cortex at the implicit and explicit AER. The findings of this study demonstrate that inhibition words can automatically and effectively reduce an individual's negative emotion experience, and left ventrolateral prefrontal cortex and left dorsolateral prefrontal cortex have been both implicated in self-control during AER.
Alterations in fear learning/generalization are considered to be relevant mechanisms engendering the development of anxiety disorders being the most prevalent mental disorders. Although anxiety disorders almost exclusively have their first onset in childhood and adolescence, etiological research focuses on adult individuals. In this study, we evaluated findings of a recent meta-analysis of genome-wide association studies in adult anxiety disorders with significant associations of four single nucleotide polymorphisms (SNPs) in a large cohort of 347 healthy children (8-12 years) characterized for dimensional anxiety. We investigated the modulation of anxiety parameters by these SNPs in a discriminative fear conditioning and generalization paradigm in the to-date largest sample of children. Results extended findings of the meta-analysis showing a genomic locus on 2p21 to modulate anxious personality traits and arousal ratings. These SNPs might, thus, serve as susceptibility markers for a shared risk across pathological anxiety, presumably mediated by alterations in arousal.
This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure. For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model. The model is trained and evaluated on the Penn Discourse Treebank 2 corpus. While it is not as good as knowledge-intensive approaches, it clearly outperforms other models that are also trained without additional linguistic features.
This Sentiment analysis is mainly found in the user's social platform for a hot event or product point of view and attitude. Most existing sentiment analysis approaches heavily rely on a large amount of labeled data that usually involve time-consuming and error-prone manual annotations. In order to avoid the dependence on the manual annotation dictionary and reduce the human intervention in the machine learning process, In this paper, Based on the researches on sentiment analysis and deep learning, we propose a hybrid framework AM-Bi-LSTM that combines Attention Mechanism and Bi-directional Long-Short-Term Memory (Bi-LSTM) neural networks for sentence classification. We demonstrate the effectiveness and efficiency of our approach on a representative Stanford Sentiment Treebank (SST) dataset. For SST-1 and SST-2, Compared with the currently published state-of-the-art methods Conv-RNN, the accuracy of AM-Bi-LSTM is improved by 2.787% and 1.946% respectively.
In this article, we tackle the issue of the limited quantity of manually\nsense annotated corpora for the task of word sense disambiguation, by\nexploiting the semantic relationships between senses such as synonymy,\nhypernymy and hyponymy, in order to compress the sense vocabulary of Princeton\nWordNet, and thus reduce the number of different sense tags that must be\nobserved to disambiguate all words of the lexical database. We propose two\ndifferent methods that greatly reduces the size of neural WSD models, with the\nbenefit of improving their coverage without additional training data, and\nwithout impacting their precision. In addition to our method, we present a WSD\nsystem which relies on pre-trained BERT word vectors in order to achieve\nresults that significantly outperform the state of the art on all WSD\nevaluation tasks.\n
This present pilot study investigates the relationship between dependency distance and frequency based on the analysis of an English dependency treebank. The preliminary result shows that there is a non-linear relation between dependency distance and frequency. This relation between them can be further formalized as a power law function which can be used to predict the distribution of dependency distance in a treebank.
Perceived self-efficacy refers to a subject's expectation about the outcomes his/her behavior will have in a challenging situation. Low self-efficacy has been implicated in the origins and maintenance of phobic behavior. Correlational studies suggest an association between perceived self-efficacy and learning. The experimental manipulation of perceived self-efficacy offers an interesting approach to examine the impact of self-efficacy beliefs on cognitive and emotional functions. Recently, a positive effect of an experimentally induced increased self-efficacy on associative learning has been demonstrated. Changes in associative learning constitute a central hallmark of pathological fear and anxiety. Such alterations in the acquisition and extinction of conditioned fear may be related to cognitive and neurobiological factors that predict a certain vulnerability to anxiety disorders. The present study builds on previous own work by investigating the effect of an experimentally induced low perceived self-efficacy on fear acquisition, extinction and extinction retrieval in a differential fear conditioning task. Our results suggest that a negative verbal feedback, which leads to a decreased self-efficacy, is associated with changes in the acquisition of conditioned fear. During fear acquisition, the negative verbal feedback group showed decreased discrimination of fear responses between the aversive and safe conditioned stimuli (CS) relative to a group receiving a neutral feedback. The effects of the negative verbal feedback on the acquisition of fear discrimination learning were indexed by an impaired ability to discriminate the probability of receiving a shock during acquisition upon presentation of the aversive (CS+) relative to the safe stimuli (CS-). However, the effects of low self-efficacy on discrimination learning were limited to fear acquisition. No differences between the groups were observed during extinction and extinction retrieval. Furthermore, analysis of other outcome measures, i.e., skin conductance responses and CS valence ratings, revealed no group differences during the different phases of fear conditioning. In conclusion, lower perceived self-efficacy alters cognitive/expectancy components of discrimination during fear learning but not evaluative components and physiological responding. The pattern of findings suggests a selective, detrimental role of low(er) self-efficacy on the subject's ability to learn the association between ambiguous cues and threat/safety.
OBJECTIVE: Biased attention for disorder-relevant information plays a crucial role in the maintenance of different mental disorders including eating disorders and might be of use to define recovery beyond symptom-related criteria. METHOD: We assessed attention deployment using eye tracking in a cued choice viewing paradigm to two different categories of disorder-relevant stimuli in 24 individuals with acute anorexia nervosa (AN), 20 weight-recovered individuals with a history of AN (WRAN) and 23 healthy control participants (CG). Picture pairs consisted of a food stimulus or a picture depicting physical activity and a matched control stimulus (household item/physical inactivity). Participants rated the valence of stimuli afterwards. RESULTS: The groups did not differ in initial attention deployment. In later processing stages, AN patients showed a generalized attentional avoidance of food and control pictures as compared to CG, while WRAN individuals were in between. AN patients showed an attentional bias toward physical activity pictures as compared to WRAN individuals, but not the CG. AN individuals rated the food pictures and the pictures showing physical inactivity as less pleasant than the CG, while WRAN individuals were in between. DISCUSSION: Attention deployment is partly changed in WRAN as compared to the acute AN group, especially with regard to a shift away from illness-compatible stimuli (physical activity), and this might be a useful recovery criterion. Valence rating of food stimuli might be an additional useful tool to distinguish between acutely ill and weight-recovered individuals. Attentional biases for illness-compatible stimuli might qualify as a valuable approach to defining recovery in AN.
This paper is a linguistic as well as technical survey for the development of a shallow discourse parser for Czech. It focuses on long-distance discourse relations signalled by (mostly) anaphoric discourse connectives. Proceeding from the division of connectives on “structural” and “anaphoric” according to their (in)ability to accept distant (non-adjacent) text segments as their left-sided arguments, and taking into account results of related analyses on English data in the framework of the Penn Discourse Treebank, we analyze a large amount of language data in Czech. We benefit from the multilayer manual annotation of various language aspects from morphology to discourse, coreference and bridging relations in the Prague Dependency Treebank 3.0. We describe the linguistic parameters of long-distance discourse relations in Czechin connection with their anchoring connective, and suggest possible ways of their detection. Our empirical research also outlines some theoretical consequences for the underlying assumptions in discourse analysis and parsing, e.g. the risk of relying too much on different (language-specific?) part-of-speech categorizations of connectives or the different perspectives in shallow and global discourse analyses (the minimality principle vs. higher text structure).
We present a comparative analysis of PP ordering in English and (Mandarin) Chinese, two languages with distinct typological word order characteristics. Previous work on PP orderings have mainly focused on English using data of relatively small size. Here we leverage corpora of much larger scale with straightforward annotations. We use the Penn Treebank for English, which includes three corpora that cover both written and spoken domains, and the Chinese Penn Treebank for Chinese. We explore the individual effect of dependency length, the argument status of the PP (argument or adjunct) and the traditional adverbial ordering rule, Manner before Place before Time. In addition, we evaluate the predictive power of dependency length and argument status with weights estimated from logistic regression models. We show that while dependency length plays a strong role across genre for English, it only exerts a mild effect in Chinese. On the other hand, the argument status of the PP has a pronounced role in both languages, that is, there exists a strong tendency for the argument-like PP to appear closer to the head verb than the adjunct-like PP. Our work contributes empirically to the long-standing proposal in linguistic typology that crosslinguistic word ordering preference is driven by cooperating and competing principles.
We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3% over a state-of-the-art method.
The paper aims to examine how the acoustic input (the surface form) and the abstract linguistic representation (the underlying representation) interact during spoken word recognition by investigating left-dominant tone sandhi, a tonal alternation in which the underlying tone of the first syllable spreads to the sandhi domain. We conducted two auditory-auditory priming lexical decision experiments on Shanghai left-dominant sandhi words with less-frequent and frequent Shanghai users, in which each disyllabic target was preceded by monosyllabic primes either sharing the same underlying tone, surface tone, or being unrelated to the tone of the first syllable of the sandhi targets. Results showed a surface priming effect but not an underlying priming effect in younger speakers who used Shanghai less frequently, but no surface or underlying priming effect in older speakers who used Shanghai more often. Moreover, the surface priming did not interact with speakers' familiarity ratings to the sandhi targets. These patterns suggest that left-dominant Shanghai sandhi words may be represented in the sandhi form in the mental lexicon. The results are discussed in the context of how phonological opacity, productivity, the non-structure-preserving nature of tone spreading, and speakers' semantic knowledge influence the representation and processing of tone sandhi words.
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple BERT-based LS approach that makes use of the pre-trained unsupervised deep bidirectional representations BERT. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement than these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 11 Accuracy points on three well-known benchmarks.
Borderline personality disorder (BPD) is a diagnosis characterized by intense and labile emotion; dialectical behavior therapy, a common treatment for BPD, aims to reduce the intensity and lability of clients' emotion through multiple methods, some of which occur in the therapy session, with the expectation that changes will generalize to the rest of clients' lives. However, little research has examined how BPD clients' affect presents and varies in session or whether affect in session reflects patients' patterns of affect outside of treatment. This study had 2 aims: (a) to explore changes in clients' positive and negative affect in therapy, and (b) to assess if the severity of client psychopathology relates to affect in treatment. Positive and negative affect ratings were collected from clients (N = 73) at the start and end of every individual therapy session (total sessions = 1,474). Hierarchical linear modeling and linear regression were used to examine patterns of affect and assess the relationship between affect and severity. Results indicated that positive affect increased while negative affect decreased between the start and end of sessions, with the same pattern of change in presession affect from week to week. In addition, increased BPD severity was associated with lower presession positive affect ratings and higher negative affect ratings. Further exploration is needed to assess which dialectical behavior therapy treatment processes contribute to changes in in-session affect and how in-session affect relates to treatment outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Besides, LIMIT-BERT adopts linguistics mask strategy: Syntactic and Semantic Phrase Masking which mask all of the tokens corresponding to a syntactic/semantic phrase. Different from recent Multi-Task Deep Neural Networks (MT-DNN) (Liu et al., 2019), our LIMIT-BERT is linguistically motivated and learning in a semi-supervised method which provides large amounts of linguistic-task data as same as BERT learning corpus. As a result, LIMIT-BERT not only improves linguistic tasks performance but also benefits from a regularization effect and linguistic information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT obtains new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.
Abstract Generating novel design concepts is a cornerstone for producing innovative products. Although many methods have been proposed for supporting the task, their performance depends on human ability. The goal of this research is to build a method supporting designers to generate novel design concepts with the knowledge of what factors have positive effects on the novelty. Toward the goal, this research assumes that the more distant two function concepts chosen, the more novel idea would come up with by the combination of the two concepts. Based on the assumption, this paper introduces a notion of novelty potential of the combination of two function concepts, and proposes a method to assess it by the function similarity. It is calculated with the integration of a lexical database for natural language called WordNet and a distributional semantics method called word2vec. The proposed method is adapted to case studies in which students perform design concept generation for given design tasks. The correlation analysis is performed to verify the assessment performance of the proposed method. This paper discusses its possibility based on the results of the case studies.
Encoding and retrieval of emotionally arousing stimuli depend on the activation of multiple interconnected brain regions, with people showing differences in their individual strength of emotional perception and recollection. Understanding the association between these brain regions and the behavioral outcome might therefore have important clinical implications as dysfunctional emotional memory processes are characteristic of many psychiatric disorders. Based on behavioral and fMRI data collected from healthy young adults (N = 1'385), we investigated brain activation patterns, arousal ratings and memory performance during encoding and retrieval of negative and neutral pictures. We performed multi-voxel pattern analysis (MVPA) and voxel-wise association analyses. Subjects' individual strength of perceived arousal at encoding and subjects' memory performance at recognition could be predicted from the fMRI data of the respective tasks by using a topographically identical network of brain regions. This network was mainly left lateralized including dense clusters of voxels in the occipital and parietal lobe and including the amygdala. Voxel-wise association analyses confirmed the close link between the brain activation of both tasks and their relation to the respective behavioral outcome. These results point to the importance of the here identified brain network for emotional memory processes in health and, possibly, disease.
There are few studies of user interaction with music libraries comprising solely of unfamiliar music, despite such music being represented in national music information centre collections. We aim to develop a system that encourages exploration of such a library. This study investigates the influence of 69 users’ pre-existing musical genre and feature preferences on their ongoing continuous real-time psychological affect responses during listening and the acoustic features of the music on their liking and familiarity ratings for unfamiliar art music (the collection of the Australian Music Centre) during a sequential hybrid recommender-guided interaction. We successfully mitigated the unfavorable starting conditions (no prior item ratings or participants’ item choices) by using each participant’s pre-listening music preferences, translated into acoustic features and linked to item view count from the Australian Music Centre database, to choose their seed item. We found that first item liking/familiarity ratings were on average higher than the subsequent 15 items and comparable with the maximal values at the end of listeners’ sequential responses, showing acoustic features to be useful predictors of responses. We required users to give a continuous response indication of their perception of the affect expressed as they listened to 30-second excerpts of music, with our system successfully providing either a “similar” or “dissimilar” next item, according to—and confirming—the utility of the items’ acoustic features, but chosen from the affective responses of the preceding item. We also developed predictive statistical time series analysis models of liking and familiarity, using music preferences and preceding ratings. Our analyses suggest our users were at the starting low end of the commonly observed inverted-U relationship between exposure and both liking and perceived familiarity, which were closely related. Overall, our hybrid recommender worked well under extreme conditions, with 53 unique items from 100 chosen as “seed” items, suggesting future enhancement of our approach can productively encourage exploration of libraries of unfamiliar music.
Purpose This paper aims to describe the structure of an aligned Serbian-German literary corpus (SrpNemKor) contained in a digital library Bibliša. The goal of the research was to create a benchmark Serbian-German annotated corpus searchable with various query expansions. Design/methodology/approach The presented research is particularly focused on the enhancement of bilingual search queries in a full-text search of aligned SrpNemKor collection. The enhancement is based on using existing lexical resources such as Serbian morphological electronic dictionaries and the bilingual lexical database Termi. Findings For the purpose of this research, the lexical database Termi is enriched with a bilingual list of German-Serbian translated pairs of lexical units. The list of correct translation pairs was extracted from SrpNemKor, evaluated and integrated into Termi. Also, Serbian morphological e-dictionaries are updated with new entries extracted from the Serbian part of the corpus. Originality/value A bilingual search of SrpNemKor in Bibliša is available within the user-friendly platform. The enriched database Termi enables semantic enhancement and refinement of user’s search query based on synonyms both in Serbian and German at a very high level. Serbian morphological e-dictionaries facilitate the morphological expansion of search queries in Serbian, thereby enabling the analysis of concepts and concept structures by identifying terms assigned to the concept, and by establishing relations between terms in Serbian and German which makes Bibliša a valuable Web tool that can support research and analysis of SrpNemKor.
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep token representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces the total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves the efficiency of the Transformer model by about 1.4 times while delivering similar performance.
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.
SUD is an annotation scheme for syntactic dependency treebanks, near isomorphic to UD (Universal Dependencies). Contrary to UD, it is based on syntactic criteria (favoring functional heads) and the relations are defined on distributional and functional bases. In this paper, we will recall and specify the general principles underlying SUD, present the updated set of SUD relations, discuss the central question of MWEs, and introduce an orthogonal layer of deep-syntactic features converted from the deep-syntactic part of the UD scheme.
Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory. We also introduce a new Gated Recursive Cell to compose lower-level representations into higher-level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature.
Cultural norms for the experience, expression, and regulation of emotion vary widely between individualistic and collectivistic cultures. Collectivistic cultures value conformity, social harmony, and social status hierarchies, which demand sensitivity and focus to broader social contexts, such that attention is directed to contextual emotion information to effectively function within constrained social roles and suppress incongruent personal emotions. By contrast, individualistic cultures valuing autonomy and personal aspirations are more likely to attend to central emotion information and to reappraise emotions to avoid negative emotional experience. Here we examined how culture affects perceptual strategies employed during emotion regulation, particularly during cognitive reappraisal and emotional suppression. Eye movements were measured while healthy young adult participants viewed negative International Affective Picture System (IAPS) images and regulated emotions by using either strategies of reappraisal (19 Asian American, 21 Caucasian American) or suppression (21 Asian American, 23 Caucasian American). After image viewing, participants rated how negative they felt as a measure of subjective emotional experience. Consistent with prior studies, reappraisers made lower negative valence ratings after regulating emotions than suppressers across both Asian American and Caucasian American groups. Although no cultural variation was observed in subjective emotional experience during emotion regulation, we found evidence of cultural variation in perceptual strategies used during emotion regulation. During middle and late time periods of emotional suppression, Asian American participants made significantly fewer fixations to emotionally salient areas than Caucasian American participants. These results indicate cultural variation in perceptual differences underlying emotional suppression, but not cognitive reappraisal.
Language standardization processes and hegemonic mechanisms are responsible for establishing the linguistic “norm” taught in schools and recognized by educated and trustworthy individuals. Through these processes and mechanisms, specific phonetic characteristics (otherwise natural to the phonetic inventory of particular languages) are identified as “non-prestigious” elements removed from the standard. Taking a sociolinguistic approach, this chapter offers a comparative review of selected phonetic characteristics, such as gheada, seseo, yeísmo, and so forth, present in Galician, Portuguese, and/or Spanish in order to shed light on the role played by institutions and/or the linguistic market in the (de-)construction of language identities.
Cross-lingual dependency parsing involves transferring syntactic knowledge\nfrom one language to another. It is a crucial component for inducing dependency\nparsers in low-resource scenarios where no training data for a language exists.\nUsing Faroese as the target language, we compare two approaches using\nannotation projection: first, projecting from multiple monolingual source\nmodels; second, projecting from a single polyglot model which is trained on the\ncombination of all source languages. Furthermore, we reproduce multi-source\nprojection (Tyers et al., 2018), in which dependency trees of multiple sources\nare combined. Finally, we apply multi-treebank modelling to the projected\ntreebanks, in addition to or alternatively to polyglot modelling on the source\nside. We find that polyglot training on the source languages produces an\noverall trend of better results on the target language but the single best\nresult for the target language is obtained by projecting from monolingual\nsource parsing models and then training multi-treebank POS tagging and parsing\nmodels on the target side.\n
OBJECTIVE: Cognitive-behavioral therapy (CBT) has been hypothesized to act by reducing the pathologically enhanced semantic, anxiety-related associations of patients with panic disorder. This study investigated the effects of CBT on the behavioral and neural correlates of the panic-related semantic network in patients with panic disorder. METHODS: An automatic semantic priming paradigm specifically tailored for panic disorder, in which panic symptoms (e.g., "dizziness") were primed by panic triggers (e.g., "elevator") compared with neutral words (e.g., "bottle"), was performed during functional MRI scanning with 118 patients with panic disorder (compared with 150 healthy control subjects) before and 42 patients (compared with 52 healthy control subjects) after an exposure-based CBT. Neural correlates were investigated by comparing 103 pairs of matched patients and control subjects at the baseline (for patients) or T1 (for control subjects) assessment and 39 pairs at the posttreatment or T2 assessment. RESULTS: At baseline or T1, patients rated panic-trigger/panic-symptom word pairs with higher relatedness and higher negative valence compared with healthy control subjects. Patients made faster lexical decisions to the panic-symptom words when they were preceded by panic-trigger words. This panic-priming effect in patients (compared with control subjects) was reflected in suppressed neural activation in the left and right temporal cortices and insulae and enhanced activation in the posterior and anterior cingulate cortices. After CBT, significant clinical improvements in the patient group were observed along with a reduction in relatedness and negative valence rating and attenuation of neural activation in the anterior cingulate cortex for processing of panic-trigger/panic-symptom word pairs. CONCLUSIONS: The findings support a biased semantic network in panic disorder, which is normalized after CBT. Attenuation of anterior cingulate cortex activation for processing of panic-related associations provides a potential mechanism for future therapeutic interventions.
Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotated target data nor parallel corpora are available. In this paper, we focus on methods for cross-lingual transfer to distant languages and propose to learn a generative model with a structured prior that utilizes labeled source data and unlabeled target data jointly. The parameters of source model and target model are softly shared through a regularized log likelihood objective. An invertible projection is employed to learn a new interlingual latent embedding space that compensates for imperfect crosslingual word embedding input. We evaluate our method on two syntactic tasks: part-ofspeech (POS) tagging and dependency parsing. On the Universal Dependency Treebanks, we use English as the only source corpus and transfer to a wide range of target languages. On the 10 languages in this dataset that are distant from English, our method yields an average of 5.2% absolute improvement on POS tagging and 8.3% absolute improvement on dependency parsing over a direct transfer method using state-of-the-art discriminative models. 1 3 Following Ahmad et al. ( fastText_multilingual, which contains alignment matrices for 78 languages, which also allows comparison with their numbers in Section 4.3.
Neural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to encode sentiment class labels. To this end, we investigate two formalisms with deep sentiment representations that capture sentiment subtype expressions by latent variables and Gaussian mixture vectors, respectively. Experiments on Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar over vanilla neural encoders. Using ELMo embeddings, our method gives the best results on this benchmark.
Topic Modeling encompasses a set of techniques for text clustering and tag recommendation with significant advantages such as unsupervised learning. Based on Latent Dirichlet Allocation (LDA) topic modeling, every single word is related to a set of topics with different weight. The weights are furt her estimated in order to determine the semantic relation between the words and the rest of the documents. Apparently the chief drawback of topic modeling techniques, specifically LDA, lies on their incapability in clustering short texts in which semantic relation between words is neglected. This issue is deemed more severe when analyzing social networks such as Twitter wherein short texts are the case. It is assumed that semantic relation between a document and the target short text helps obtain efficient clustering of short texts via topic modeling. Hence, the current paper proposes a method of topic modeling named Semantic Knowledge LDA based on semantic relations between the words in tweets from Twitter social network based on the co-occurrence of words. Additionally, we propose a method of hashtag recommender system based on topic vector (TV) text similarity, named TV based Hashtag Recommender System (TVHRS). Accordingly, we applied our word co-occurrence LDA (SKLDAC) method together with WordNet lexical database to cluster the short texts from Twitter. The clustered topics are later used as the repository for the proposed hashtag recommender system. The proposed system of both SKLDA and TVHRS were applied to a set of 12,309,911 real tweets for testing purposes. When comparing the components of the proposed system to the existing methods, we recorded higher performance in terms of precision, recall and F-Score of 0.551, 0.682 and 0.526, respectively.
This paper investigates which annotation scheme of dependency treebank is more congruent for the measurement of syntactic complexity and cognitive constraint of language materials. Two representatives of semantic-and syntactic-oriented annotation schemes, the Universal Dependencies (UD) and the Surface-Syntactic Universal Dependencies (SUD), are under discussion. The results show that, on the one hand, natural languages based on both annotation schemes follow the universal linguistic law of Dependency Distance Minimization (DDM); on the other hand, according to the metric of Mean Dependency Distances (MDDs), the SUD annotation scheme that accords with traditional dependency syntaxes are more expedient to measure syntactic difficulty and cognitive demand.
The lack of understanding and definitional inconsistencies regarding agritourism and the importance of cooperation in sustaining this kind of tourism are underlined in the literature. This study analyzes the perceptions of agritourism and cooperation from actors in the sector using a plurality of methods, including unsupervised (a) text mining and (b) sentiment analysis with the use of a lexical database, as well as (c) supervised qualitative data analysis. Based on the assumption that destinations with different geographic characteristics have different features and products, two different destinations as for its accessibility and tourism recognition were selected for comparison: (a) an island—Lesvos in the North Aegean Sea, and (b) a continental mountain region—Plastiras Lake, in Greece. The data were collected from personal in-depth interviews and with the use of semi-structured questionnaires. From a methodological perspective, all three methods provided unique insights on the study’s themes, and the overall image of agritourism and cooperation was positive. A common understanding seems important for cooperation and networking; however, training is needed not only for effective promotion of agritourism, but also for cooperation techniques, benefits, trust-building mechanisms and best practices.
Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data. Sequential data has long sequences that hold relationships. RNN can handle long sequences but suffers from the vanishing and exploding gradient problems. While LSTM and other memory networks address this problem, they are not capable of handling long sequences (50 or more data points long sequence patterns). Language modelling requiring learning from longer sequences are affected by the need for more information in memory. This paper introduces Long Term Memory network (LTM), which can tackle the exploding and vanishing gradient problems and handles long sequences without forgetting. LTM is designed to scale data in the memory and gives a higher weight to the input in the sequence. LTM avoid overfitting by scaling the cell state after achieving the optimal results. The LTM is tested on Penn treebank dataset, and Text8 dataset and LTM achieves test perplexities of 83 and 82 respectively. 650 LTM cells achieved a test perplexity of 67 for Penn treebank, and 600 cells achieved a test perplexity of 77 for Text8. LTM achieves state of the art results by only using ten hidden LTM cells for both datasets.
Composers convey emotion through music by co-varying structural cues. Although the complex interplay provides a rich listening experience, this creates challenges for understanding the contributions of individual cues. Here we investigate how three specific cues (attack rate, mode, and pitch height) work together to convey emotion in Bach's Well Tempered-Clavier (WTC). In three experiments, we explore responses to (1) eight-measure excerpts and (2) musically “resolved” excerpts, and (3) investigate the role of different standard dimensional scales of emotion. In each experiment, thirty nonmusician participants rated perceived emotion along scales of valence and intensity (Experiments 1 & 2) or valence and arousal (Experiment 3) for 48 pieces in the WTC. Responses indicate listeners used attack rate, Mode, and pitch height to make judgements of valence, but only attack rate for intensity/arousal. Commonality analyses revealed mode predicted the most variance for valence ratings, followed by attack rate, with pitch height contributing minimally. In Experiment 2 mode increased in predictive power compared to Experiment 1. For Experiment 3, using “arousal” instead of “intensity” showed similar results to Experiment 1. We discuss how these results complement and extend previous findings of studies with tightly controlled stimuli, providing additional perspective on complex issues of interpersonal communication.
Transfer parsing has been used for developing dependency parsers for languages with no treebank by using transfer from treebanks of other languages (source languages). In delexicalized transfer, parsed words are replaced by their part-of-speech tags. Transfer parsing may not work well if a language does not follow uniform syntactic structure with respect to its different constituent patterns. Earlier work has used information derived from linguistic databases to transform a source language treebank to reduce the syntactic differences between the source and the target languages. We propose a transformation method where a source language pattern is transformed stochastically to one of the multiple possible patterns followed in the target language. The transformed source language treebank can be used to train a delexicalized parser in the target language. We show that this method significantly improves the average performance of single-source delexicalized transfer parsers. We also show that, in the multi-source settings, parsers trained using a concatenation of transformed source language treebanks work better when a subset of the source language treebanks is used rather than concatenating all of them or only one. However, the problem of selecting the subset of treebanks whose combination gives the best-performing parser from the set of all the available treebanks is hard. We propose a greedy selection heuristic based on the labelled attachment scores of the corresponding single-source parsers trained using the treebanks after transformation.
Learning hierarchical abstractions from sequences is a challenging and open problem for recurrent neural networks (RNNs). This is mainly due to the difficulty of detecting features that span over long time distances with also different frequencies. In this paper, we address this challenge by introducing surprisal-based activation, a novel method to preserve activations and skip updates depending on encoding-based information content. The preserved activations can be considered as temporal shortcuts with perfect memory. We present a preliminary analysis by evaluating surprisal-based activation on language modeling with the Penn Treebank corpus and find that it can improve performance when compared to baseline RNNs and Long Short-Term Memory (LSTM) networks.
Clinical motor and non-motor effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) seem to depend on the stimulation site within the STN. We analysed the effects of the position of the stimulation electrode within the motor STN on subjective emotional experience, expressed as emotional valence and arousal ratings to pictures representing primary rewards and aversive fearful stimuli in 20 PD patients. Patients' ratings from both aversive and erotic stimuli matched the mean ratings from a group of 20 control subjects at similar position within the STN. Patients with electrodes located more posteriorly reported both valence and arousal ratings from both the rewarding and aversive pictures as more extreme. Moreover, posterior electrode positions were associated with a higher occurrence of depression at a long-term follow-up. This brain-behavior relationship suggests a complex emotion topography in the motor part of the STN. Both valence and arousal representations overlapped and were uniformly arranged anterior-posteriorly in a gradient-like manner, suggesting a specific spatial organization needed for the coding of the motivational salience of the stimuli. This finding is relevant for our understanding of neuropsychiatric side effects in STN DBS and potentially for optimal electrode placement.
Purpose: The purpose of this paper is to prove the importance of understanding body language to achieve the effectiveness of English language classes.
 Methodology: Literature investigation is carried out to confirm the objective of this paper.
 Results: In the teaching and learning process, effective communication between a teacher and students is the utmost importance. The failure to establish effective communication in the classroom setting will result in a deficiency of the teaching and learning process.
 Implications: It is the fact that many cues of body language are culture-specific and therefore the only way to improve the understanding of body language is by interacting with people from different cultural backgrounds so that they can share socio-cultural and linguistic norms. Thus, the experience will enrich the teacher with cross-cultural nonverbal behavior which benefits his performance in the classroom. Both teachers' and students' knowledge of non-verbal language play very significant roles in making the classroom interaction successful. Therefore, finally, a summary is presented to reconfirm the importance of integrating body language into classroom interaction.
In this paper we present a novel lemmatization method based on a\nsequence-to-sequence neural network architecture and morphosyntactic context\nrepresentation. In the proposed method, our context-sensitive lemmatizer\ngenerates the lemma one character at a time based on the surface form\ncharacters and its morphosyntactic features obtained from a morphological\ntagger. We argue that a sliding window context representation suffers from\nsparseness, while in majority of cases the morphosyntactic features of a word\nbring enough information to resolve lemma ambiguities while keeping the context\nrepresentation dense and more practical for machine learning systems.\nAdditionally, we study two different data augmentation methods utilizing\nautoencoder training and morphological transducers especially beneficial for\nlow resource languages. We evaluate our lemmatizer on 52 different languages\nand 76 different treebanks, showing that our system outperforms all latest\nbaseline systems. Compared to the best overall baseline, UDPipe Future, our\nsystem outperforms it on 62 out of 76 treebanks reducing errors on average by\n19% relative. The lemmatizer together with all trained models is made available\nas a part of the Turku-neural-parsing-pipeline under the Apache 2.0 license.\n
This article presents an analysis of experiments with statistical and neural parsing techniques for Urdu, a widely spoken South Asian language. We demonstrate state of the art constituency parsing results for an Urdu treebank. Urdu is a morphologically rich and is characterized by free word order. Language representation (e.g. input type, lemmatization, word clusters), part of speech tag set, phrase labels and the size of a training corpus are crucial for parsing such languages. In this article, probabilistic context-free grammars, data-oriented parsing, and recursive neural network based models have been experimented with several linguistic features which show improvements in the parsing results. Features include syntactic sub-categorization of POS tags, empirically learned horizontal and vertical markovizations and lexical head words. These features enable dependency information for case markers and add phrasal and lexical context to the parse trees. The data-oriented parsing and recursive neural network model give an f-score of 87.1 by considering gold POS tags in the test set, on textual input, they show a performance with f-scores of 83.4 and 84.2, respectively. To overcome the issue of data sparsity due to the morphological richness, lemmatization and unsupervised word clustering have been performed. A treebank should cover most probable word orders of the language so that models can learn various orders accurately. To analyze the order coverage of the treebank and learning capability of different parsers, a test set has been prepared conditioning different word orders. This test set is evaluated with the best performing parsing models and with gold POS tags, f-scores are above 90 and on textual input, the average f-score is 87.6.
Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.
Several studies have attempted to investigate how the brain codes emotional value when processing music of contrasting levels of dissonance; however, the lack of control over specific musical structural characteristics (i.e., dynamics, rhythm, melodic contour or instrumental timbre), which are known to affect perceived dissonance, rendered results difficult to interpret. To account for this, we used functional imaging with an optimized control of the musical structure to obtain a finer characterization of brain activity in response to tonal dissonance. Behavioral findings supported previous evidence for an association between increased dissonance and negative emotion. Results further demonstrated that the manipulation of tonal dissonance through systematically controlled changes in interval content elicited contrasting valence ratings but no significant effects on either arousal or potency. Neuroscientific findings showed an engagement of the left medial prefrontal cortex (mPFC) and the left rostral anterior cingulate cortex (ACC) while participants listened to dissonant compared to consonant music, converging with studies that have proposed a core role of these regions during conflict monitoring (detection and resolution), and in the appraisal of negative emotion and fear-related information. Both the left and right primary auditory cortices showed stronger functional connectivity with the ACC during the dissonant portion of the task, implying a demand for greater information integration when processing negatively valenced musical stimuli. This study demonstrated that the systematic control of musical dissonance could be applied to isolate valence from the arousal dimension, facilitating a novel access to the neural representation of negative emotion.
The affect associated with negative events fades faster than the affect associated with positive events (the fading affect bias). The fading affect bias is present in most participants and is thought to be evidence of a healthy coping mechanism operating in autobiographical memory. Prior research shows that the fading affect bias can be distorted by negative individual difference variables such as dysphoria and anxiety. The goal of this research is to link the fading affect bias to the positive individual difference variable of Grit. A total of 197 participants completed the short Grit Scale and were divided into four groups based on their Grit scores (i.e., low Grit to high Grit). Participants retrieved positive and negative event memories and then made affect ratings for the events. The results show that increased levels of Grit were associated with a stronger fading affect bias.
Recently, it has been shown that various auditory stimuli modulate flavour perception. The present study attempts to understand the effects of environmental sounds (park, food court, fast food restaurant, cafe, and bar sounds) on the perception of chocolate gelato (specifically, sweet, bitter, milky, creamy, cocoa, roasted, and vanilla notes) using the Temporal Check-All-That-Apply (TCATA) method. Additionally, affective ratings of the auditory stimuli were obtained using the Self-Assessment Manikin (SAM) in terms of their valence, arousal, and dominance. In total, 58 panellists rated the sounds and chocolate gelato in a sensory laboratory. The results revealed that bitterness, roasted, and cocoa notes were more evident when the bar, fast food, and food court sounds were played. Meanwhile, sweetness was cited more in the early mastication period when listening to park and café sounds. The park sound was significantly higher in valence, while the bar sound was significantly higher in arousal. Dominance was significantly higher for the fast food restaurant, food court, and bar sound conditions. Intriguingly, the valence evoked by the pleasant park sound was positively correlated with the sweetness of the gelato. Meanwhile, the arousal associated with bar sounds was positively correlated with bitterness, roasted, and cocoa attributes. Taken together, these results clearly demonstrate that people's perception of the flavour of gelato varied with the different real-world sounds used in this study.
In the past decades, linguistic typology went through a renewing phase that involved a significant change in the research questions and methods of the discipline, which is now interested in fine-grained features underlying language diversity. In this paper, we propose a novel approach to address the newly defined needs of linguistic typology by extracting qualitative and quantitative information about a wide range of features from multilingual annotated corpora based on Natural Language Processing methods and techniques. We tested our method in a case study focusing on word order variation in two widely investigated constructions, VERB-SUBJ(ect) and NOUN-ADJ(ective), with a specific view to structural and functional factors underlying the preference for one or the other order, both intra- and cross-linguistically, and their interaction. Preliminary experiments have been carried out aimed at acquiring typological evidence from a selection of linguistically annotated treebanks for three different languages, namely Italian, Spanish and English. Our results show the effectiveness of the method in letting similarities and differences also emerge from typologically close languages.
We present a novel semantic framework for modeling linguistic expressions of\ngeneralization---generic, habitual, and episodic statements---as combinations\nof simple, real-valued referential properties of predicates and their\narguments. We use this framework to construct a dataset covering the entirety\nof the Universal Dependencies English Web Treebank. We use this dataset to\nprobe the efficacy of type-level and token-level information---including\nhand-engineered features and static (GloVe) and contextual (ELMo) word\nembeddings---for predicting expressions of generalization. Data and code are\navailable at decomp.io.\n