Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Heart Rate Variability (HRV) has been widely studied in laboratory settings due to its clinical implications, primarily as a potential biomarker of emotion regulation (ER). Studies have reported that individuals with higher resting HRV show more distinct startle reflexes to negative stimuli as compared to those with lower HRV. These responses have been associated with better defense system function when managing the context demands. There is, however, a lack of empirical evidence on the association between resting HRV and eyeblinks during laboratory tasks using instructed ER. This study explored the influence of tonic HRV on voluntary cognitive reappraisal through subjective and startle responses measured during an independent ER task. In total, 122 healthy participants completed a task consisting of attempts to upregulate, downregulate, or react naturally to emotions prompted by unpleasant pictures. Tonic HRV was measured for 5 minutes before the experiment began. Current results did not support the idea that self-reported and eyeblink responses were influenced by resting HRV. These findings suggest that, irrespective of resting HRV, individuals may benefit from strategies such as reappraisal that are useful for managing negative emotions. Experimental studies should further explore the role of individual differences when using ER strategies during laboratory tasks.
The paper is an attempt to compare Hyderabad Telugu Treebank (HTTB) and HCU-IIIT-H Telugu Treebank from a statisticalpoint of view. HTTB has 2,715 annotated sentences and HCU-IIIT-H TTB has 3,222 annotated sentences. Both the Treebanks were annotated by following Paninian Grammar Formalism proposed by Bharati, A.; Sharma, D.M.; Husain, S.; Bai, L.; Begam, R. and Sangal, R.(2009).HTTB is an inter-chunk-based treebank data. HCU-IIIT-H TTB is an intra-chunk-based treebankdata. Both the treebanks’ data size is random. Later, the paper discusses the Telugu Treebanks in detail. The paper focuses on statistical frequencies viz. POS, Chunk and Syntactic labels. VM (3807 times) and NN (5486 times) are the frequent POS labels inHTTB and HCU-IIIT-H TTB respectively. NP (7954 and 6223 times) is the frequent phrasal category in both the treebanks. The most frequent k-labels are kartā(k1) (2375-2381 times) and karma(k2) (1408-1437 times) and non-frequent label is karaṇa(k3) (17-39 times) in both the treebanks. The most frequent non-k-labels are verb modifier (vmod) (949 times) and noun modifier (nmod) (1033 times) in both the treebanks. The statistical distribution mentions the coverage of the labels (kāraka, non-kāraka) of both theTelugu treebanks. Later it discusses the comparison of both the treebanks and tries to provide the reasons for the highest and lowest frequencies in both the treebanks. k1 and k2 have 60% of the coverage in karaka labels, vmod, nmod, adv, ccof, pof also has 60% of the coverage in non-karaka labels. This kind of statistical study can help to boost the accuracy of the parser.
Abstract Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for crosslinguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.
Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a long short-term memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.
Neural networks are often over-parameterized and hence benefit from aggressive regularization. Conventional regularization methods, such as dropout or weight decay, do not leverage the structures of the network's inputs and hidden states. As a result, these conventional methods are less effective than methods that leverage the structures, such as SpatialDropout and DropBlock, which randomly drop the values at certain contiguous areas in the hidden states and setting them to zero. Although the locations of dropping areas random, the patterns of SpatialDropout and DropBlock are manually designed and fixed. Here we propose to learn the dropping patterns. In our method, a controller learns to generate a dropping pattern at every channel and layer of a target network, such as a ConvNet or a Transformer. The target network is then trained with the dropping pattern, and its resulting validation performance is used as a signal for the controller to learn from. We show that this method works well for both image recognition on CIFAR-10 and ImageNet, as well as language modeling on Penn Treebank and WikiText-2. The learned dropping patterns also transfers to different tasks and datasets, such as from language model on Penn Treebank to Engligh-French translation on WMT 2014. Our code will be available at: https://github.com/googleresearch/google-research/tree/master/auto_dropout.
У раду се даје кратак приказ теорије семантике оквира (енгл. Frame Semantics), на којој је заснована лексичка база Фрејмнет (енгл. FrameNet). Представљена је концепција ове мреже, као и могућности њене примене. Представљена је и лексичка анализа која се примењује у пројекту израде Фрејмнета и указано на разлике између анализе засноване на оквиру у односу на анализу засновану на речи. Затим је приказано неколико повезаних оквира које призивају речи из домена ризика. У раду је представљена и платформа NLTK (енгл. Natural Language Toolkit), помоћу које се могу користити разни језички ресурси, међу њима и Фрејмнет. Завршно поглавље пружа анализу именице ризик на корпусу рударства. Представљени су најчешћи колокати ове именице, скица њене употребе, конкорданце за поједине моделе, проналажење синонима и повезаних речи у виду тезауруса, графички приказ фреквенција појединих колокација, као и облака речи.
We present the first linguistically annotated treebank of Ashokan Prakrit, an early Middle Indo-Aryan dialect continuum attested through Emperor Ashoka Maurya's 3rd century BCE rock and pillar edicts. For annotation, we used the multilingual Universal Dependencies (UD) formalism, following recent UD work on Sanskrit and other Indo-Aryan languages. We touch on some interesting linguistic features that posed issues in annotation: regnal names and other nominal compounds, "proto-ergative" participial constructions, and possible grammaticalizations evidenced by sandhi (phonological assimilation across morpheme boundaries). Eventually, we plan for a complete annotation of all attested Ashokan texts, towards the larger goals of improving UD coverage of different diachronic stages of Indo-Aryan and studying language change in Indo-Aryan using computational methods.
Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU).The recent advent of end-to-end neural models, self-supervised via language modeling (LM), and their success on a wide range of LU tasks, however, questions this belief.In this work, we empirically investigate the usefulness of supervised parsing for semantic LU in the context of LM-pretrained transformer networks.Relying on the established fine-tuning paradigm, we first couple a pretrained transformer with a biaffine parsing head, aiming to infuse explicit syntactic knowledge from Universal Dependencies treebanks into the transformer.We then fine-tune the model for LU tasks and measure the effect of the intermediate parsing training (IPT) on downstream LU task performance.Results from both monolingual English and zero-shot language transfer experiments (with intermediate target-language parsing) show that explicit formalized syntax, injected into transformers through IPT, has very limited and inconsistent effect on downstream LU performance.Our results, coupled with our analysis of transformers' representation spaces before and after intermediate parsing, make a significant step towards providing answers to an essential question: how (un)availing is supervised parsing for high-level semantic natural language understanding in the era of large neural models?
International audience
Stanford Sentiment Treebank (SST) is an extension of Movie Review dataset with fine-grained labels ranging between very positive and very negative. The authors extended the MR by adding a more curated human annotation into 5 classes.<br> The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition
ДЕВДАРИАНИ Наталья Валерьевна, кандидат философских наук
We propose a theoretical reflection on the functions of linguistic norms and the tensions between the linguistic centre(s) and peripheries for any language that has undergone standardization. We propose that dialects have a right to be recognized in the language's codified norms because of the impact that standardization has on (peripheral) speakers' perceptions of, and feelings towards, their own varieties. To illustrate these ideas, we use the case of the Catalan language, which has undergone a complex and still incomplete process of standardization since the beginning of the twentieth century. After describing Catalan's current sociolinguistic situation, we analyse the recent Gramàtica de la llengua catalana (2016) by the Institut d'Estudis Catalans (GIEC). The volume approaches linguistic codification as a process of 'prescription through description'.
This paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese.It is 'general-purpose' in the sense that it is not tailored to a specific variant of CG, but rather aims to offer a theory-neutral linguistic resource (as much as possible) which can be converted to different versions of CG (specifically, CCG and Type-Logical Grammar) relatively easily.In terms of linguistic analysis, it improves over the existing Japanese CG treebank (Japanese CCGBank) on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates) for which the lexical specification of the syntactic information reflecting local dependencies turns out to be crucial.In this paper, we describe the underlying 'theory' dubbed ABC Grammar that is taken as a basis for our treebank, outline the general construction of the corpus, and report on some preliminary results applying the treebank in a semantic parsing system for generating logical representations of sentences.
Heightened responding to uncertain threat is considered a hallmark of anxiety disorder pathology. We sought to determine whether individual differences in self-reported intolerance of uncertainty (IU), a key transdiagnostic dimension in anxiety-related pathology, underlies differential recruitment of neural circuitry during cue-signalled uncertainty of threat (n = 42). In an instructed threat of shock task, cues signalled uncertain threat of shock (50%) or certain safety from shock. Ratings of arousal and valence, skin conductance response (SCR), and functional magnetic resonance imaging were acquired. Overall, participants displayed greater ratings of arousal and negative valence, SCR, and amygdala activation to uncertain threat versus safe cues. IU was not associated with greater arousal ratings, SCR, or amygdala activation to uncertain threat versus safe cues. However, we found that high IU was associated with greater ratings of negative valence and greater activity in the medial prefrontal cortex and dorsomedial rostral prefrontal cortex to uncertain threat versus safe cues. These findings suggest that during cue-signalled uncertainty of threat, individuals high in IU rate uncertain threat as aversive and engage prefrontal cortical regions known to be involved in safety-signalling and conscious threat appraisal. Taken together, these findings highlight the potential of IU in modulating safety-signalling and conscious appraisal mechanisms in situations with cue-signalled uncertainty of threat, which may be relevant to models of anxiety-related pathology.
We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used benchmark for punctuation restoration, while for Hungarian we evaluate our models on the Szeged Treebank dataset. Our best models achieve a macro-averaged $F_1$-score of 79.8 in English and 82.2 in Hungarian. Our code is publicly available.
PURPOSE: Medical education has been transformed during the COVID-19 pandemic, creating challenges regarding adequate training in ultrasound (US). Due to the discontinuation of traditional classroom teaching, the need to expand digital learning opportunities is undeniable. The aim of our study is to develop a tele-guided US course for undergraduate medical students and test the feasibility and efficacy of this digital US teaching method. MATERIALS AND METHODS: A tele-guided US course was established for medical students. Students underwent seven US organ modules. Each module took place in a flipped classroom concept via the Amboss platform, providing supplementary e-learning material that was optional and included information on each of the US modules. An objective structured assessment of US skills (OSAUS) was implemented as the final exam. US images of the course and exam were rated by the Brightness Mode Quality Ultrasound Imaging Examination Technique (B-QUIET). Achieved points in image rating were compared to the OSAUS exam. RESULTS: A total of 15 medical students were enrolled. Students achieved an average score of 154.5 (SD ± 11.72) out of 175 points (88.29 %) in OSAUS, which corresponded to the image rating using B-QUIET. Interrater analysis of US images showed a favorable agreement with an ICC (2.1) of 0.895 (95 % confidence interval 0.858 < ICC < 0.924). CONCLUSION: US training via teleguidance should be considered in medical education. Our pilot study demonstrates the feasibility of a concept that can be used in the future to improve US training of medical students even during a pandemic.
Sentiment Analysis (SA) aims to extract useful information from online Unstructured User-Generated Contents (UUGC) and classify them into positive and negative classes. State-of-the-art techniques for SA suffer a high dimensional feature space because of noisy and irrelevant features from the UUGC. Researchers have also proposed feature extraction and selection techniques to reduce high dimensional feature space, but they fall short in extracting and selecting the most effective sentiment features for sentiment model learning. Effective feature extraction and selection are significant for the SA because they can boost the learning algorithm’s predictive performance while reducing the high-dimensional feature space. To address these concerns, we propose an Intelligent Hybrid Feature Selection for Sentiment Analysis (IHFSSA) based on ensemble learning methods. IHFSSA first identifies sentiment features in the review text utilizing Penn Treebank part-of-speech tagset and integrated Wide Coverage Sentiment Lexicons (WCSL). The sentiment features subset is then selected employing a fast and simple rank-based ensemble of multiple filters feature selection method. The selected sentiment features are further refined by applying a wrapper-based backward feature selection method. Finally, for textual sentiment classification, the well-known classification algorithms Support Vector Machine (SVM), Naive Bayes (NB), Generalized Linear Model (GLM) are trained in the ensemble model on the refined sentiment feature set. The in-depth evaluation using heterogeneous domain benchmark datasets demonstrates that IHFSSA outperforms existing SA techniques.
Despite their continued popularity, categorical approaches to affect recognition have limitations, especially in real-life situations. Dimensional models of affect offer important advantages for the recognition of subtle expressions and more fine-grained analysis. We introduce a simple but effective facial expression analysis (FEA) system for dimensional affect, solely based on geometric features and Partial Least Squares (PLS) regression. The system jointly learns to estimate Arousal and Valence ratings from a set of facial images. The proposed approach is robust, efficient, and exhibits comparable performance to contemporary deep learning models, while requiring a fraction of the computational resources.
We propose the Recursive Non-autoregressive Graph-to-Graph Transformer architecture (RNGTr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dependency parsing. We demonstrate the power and effectiveness of RNGTr on several dependency corpora, using a refinement model pre-trained with BERT. We also introduce Syntactic Transformer (SynTr), a non-recursive parser similar to our refinement model. RNGTr can improve the accuracy of a variety of initial parsers on 13 languages from the Universal Dependencies Treebanks, English and Chinese Penn Treebanks, and the German CoNLL2009 corpus, even improving over the new state-of-the-art results achieved by SynTr, significantly improving the state-of-the-art for all corpora tested.
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.
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.
INTRODUCTION: The addition of graphic health warnings to cigarette packets can facilitate smoking cessation, primarily through their ability to elicit a negative affective response. Smoking has been linked to COVID-19 mortality, thus making it likely to elicit a strong affective response in smokers. COVID-19-related health warnings (C19HW) may therefore enhance graphic health warnings compared to traditional health warnings (THW). Further, because impulsivity influences smoking behaviors, we also examined whether these affective responses were associated with delay discounting. METHODS: In a between-subjects design, 240 smokers rated the valence and arousal elicited by tobacco packaging that contained either a C19HW or THW (both referring to death). Participants also completed questionnaires to quantify delay discounting, and attitudes towards COVID-19 and smoking (eg, health risks, motivation to quit). RESULTS: There were no differences between the two health warning types on either valence or arousal, nor any secondary outcome variables. There was, however, a significant interaction between health warning type and delay discounting on arousal ratings. Specifically, in smokers who exhibit low delay discounting, C19HWs elicited significantly greater subjective arousal rating than did THWs, whereas there was no significant effect of health warning type on arousal in smokers who exhibited high delay discounting. CONCLUSION: The results suggest that in smokers who exhibit low impulsivity (but not high impulsivity) C19HWs may be more arousing than THWs. Future work is required to explore the long-term utility of C19HWs, and to identify the specific mechanism by which delay discounting moderates the efficacy of tobacco health warnings. IMPLICATIONS: The study is the first to explore the impact of COVID-19-related health warnings on cigarette packaging. The results suggest that COVID-19-related warnings elicit a similar level of negative emotional arousal, relative to traditional warnings. However, COVID-19 warnings, specifically, elicit especially strong emotional responses in less impulsive smokers, who report low delay discounting. Therefore, there is preliminary evidence supporting COVID-19 related warnings for tobacco products to aid smoking cessation. Additionally, there is novel evidence that, for some warnings, high impulsiveness may be a factor in reduced warning efficacy, which may explain poorer cessation success in this population.
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.
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.
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.
We propose two fast neural combinatory models for constituency parsing: binary and multibranching. Our models decompose the bottomup parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks. These models have theoretical sub-quadratic complexity and empirical linear complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. Both the models with XLNet provide near state-of-theart accuracies for English. Syntactic branching tendency and headedness of a language are observed during the training and inference processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese).
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.
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.
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.
OBJECTIVE: Nonsuicidal self-injury (NSSI) is often cited as a key risk factor for future suicidal behavior. Capability for suicide has been repeatedly cited as an important mechanism that can account for this association. Despite this, direct tests of this hypothesis have been rare and methodologically constrained. In the present study, we conducted a direct test of this hypothesis while addressing several constraints of prior literature. METHOD: In a large sample of suicidal and self-injuring adults (n = 1,020), we tested whether changes in fearlessness about death (FAD), a core facet of the capability for suicide, accounted for the relationship between NSSI and future suicide attempts at 28-day and 2-year follow-up. FAD was assessed using the gold-standard self-report form (ACSS-FAD), an implicit test of suicide-related affect (affect misattribution paradigm-Suicide), and explicit affective ratings of suicide-relevant images. Mediation with bootstrapping was implemented to test our main hypotheses. RESULTS: As anticipated, lifetime NSSI frequency was significantly associated with suicide attempt frequency at follow-up; however, FAD failed to consistently mediate this association. Results were largely consistent across all three measures of FAD. Post hoc power analyses indicated sufficient power to detect small effects. CONCLUSIONS: Taken together, these results fail to support the hypothesis that capability for suicide explains the link between NSSI and future suicidal behavior. We discuss the implications of our results for research and theory, situating our findings in the context of recent advances in the understanding of suicide risk more broadly. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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).
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.
Disyllabic verb-noun (V-N) items in Shanghai Wu have variable surface tone patterns: They can undergo either a rightward extension tone sandhi, which extends the lexical tone of the first syllable over the entire word, or tonal reduction on the first syllable. The current study investigates how the phonological properties of these alternation processes as well as variation influence how Shanghai speakers represent and access such words. We conducted an auditory-auditory priming lexical decision experiment on Shanghai V-N items that can undergo either tonal extension or tonal reduction with native Shanghai speakers. Each disyllabic target was preceded by monosyllabic primes with the canonical tone, the tonal-extension tone, the surface tone, or a tone unrelated to the tone of the first syllable of the targets. Results showed both canonical and tonal-extension priming effects, but no surface priming effect. Moreover, although more familiar V-Ns were recognized with shorter reaction time, the priming effect did not interact with speakers’ familiarity ratings or sandhi preference ratings of the targets. These data are consistent with the interpretation that both the canonical and tonal-extension forms are represented in Shanghai speakers’ mental lexicon due to tone sandhi variation, but the representation does not seem to be modulated by the frequencies of the variants. Also, together with findings from auditory priming studies of other tone sandhi patterns, the current study suggests that certain phonological properties of an alternation, such as its locality and transparency, influence the representation of words undergoing the alternation; but whether the alternation is structure-preserving does not seem to impact the representation.
Compound probabilistic context-free grammars (C-PCFGs) have recently established a new state of the art for unsupervised phrase-structure grammar induction. However, due to the high space and time complexities of chart-based representation and inference, it is difficult to investigate C-PCFGs comprehensively. In this work, we rely on a fast implementation of C-PCFGs to conduct an evaluation complementary to that of~\citet{kim-etal-2019-compound}. We start by analyzing and ablating C-PCFGs on English treebanks. Our findings suggest that (1) C-PCFGs are data-efficient and can generalize to unseen sentence/constituent lengths; and (2) C-PCFGs make the best use of sentence-level information in generating preterminal rule probabilities. We further conduct a multilingual evaluation of C-PCFGs. The experimental results show that the best configurations of C-PCFGs, which are tuned on English, do not always generalize to morphology-rich languages.
The present study aims at comparing the effects of two subtypes of cognitive reappraisal (i.e., stimulus-focused vs. goal-based reappraisal) to reduce anticipatory anxiety of pain. Affective ratings, startle reflex, and autonomic measures (electrodermal and heart rate changes) were used as a measure of emotion regulation success. A total of 86 undergraduate students completed an anticipatory task in which they had to regulate their negative emotions or react naturally when faced with the possibility of receiving a painful thermal stimulus. Participants were randomly assigned to two experimental groups to compare the stimulus-focused and goal-based strategies explored here. Our results revealed enhanced self-reported anxiety, electrodermal activity and eyeblink response when participants tried to voluntarily down-regulate their negative emotions, compared to the control instruction. Differences between both cognitive reappraisal groups were not found. These unexpected findings suggest that brief reappraisal instructions may not necessarily be favorable for regulating emotions during anticipation of aversive events. Moreover, these results are further explained in terms of the pain expectation, the painful stimuli modality, and emotion regulation instructions.
The application value of the convolutional neural network (CNN) algorithm in the diagnosis of sports knee osteoarthropathy was investigated in this study. A network model was constructed in this experiment for image analysis of magnetic resonance imaging (MRI) technology. Then, 100 cases of sports knee osteoarthropathy patients and 50 healthy volunteers were selected. Digital radiography (DR) images and MRI images of all the research objects were collected after the inclusion of the two groups. Besides, the important physiological representations were extracted from their image data graphs, and the hidden complex relationships were learned. The state without input results was judged through convolutional network calculation, and the result prediction was given. On this basis, there was an analysis of the diagnostic efficiency of traditional DR images and MRI images based on CNN for patients with sports knee osteoarthropathy. The results showed that the MRI images analyzed by the CNN model showed a more obvious display rate than DR images for some nonbone changes of osteoarthritis. The correlation coefficient between MRI image rating and visual analog scale (VAS) was 0.865, which was higher than 0.713 of DR image rating, with a statistical meaning ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo><</mo> <mn>0.01</mn> </math> ). For cases with mild lesions, the number of cases detected by MRI based on CNN algorithm in 0–4 image rating was 15, 18, 10, 6, and 7, respectively, which was markedly better than that of DR images. In short, the MRI examination based on the CNN image analysis model could extract important physiological representations from the image data and learn the hidden complex relationships. The convolutional network was calculated to determine the state of the uninput results and give the result predictions. Moreover, MRI examination based on the CNN image analysis model had high overall diagnostic efficiency and grading diagnostic efficiency for patients with motor knee osteoarthropathy, which was of great significance in clinical practice.
The aim of this article is to identify the Old English exponent for the semantic prime LIVE following the principles of the Natural Semantic Metalanguage theory (Wierzbicka 1996, Goddard & Wierzbicka 2002, Goddard 2011). The methodology applied in the study is based on previous research in Old English semantic primes. In these terms, a search for those Old English words conveying the meaning of the semantic prime LIVE is made. This search selects the verbs (ge)buan, drohtian, (ge)eardian, (ge)libban, and wunian as candidate words for prime exponent. Then, these verbs are analysed in terms of morphological, textual, semantic, and syntactic criteria. With this purpose, relevant information on these words has been gathered from different lexicographical and textual sources in Old English, such as the Dictionary of Old English, the Dictionary of Old English Corpus, and the lexical database of Old English Nerthus. After the analysis of these verbs, the conclusion is drawn that the Old English verb (ge)libban is selected as prime exponent, as it satisfies the requirements proposed by each criterion.
This paper presents a full procedure for the development of a Part-of-Speech (POS) tagged corpus of Old Catalan. As an extremely low-resource language with rich inflection and frequent homographs, Old Catalan poses non-trivial problems in the development of a searchable constituency-based treebank. We demonstrate, however, that a semi-supervised method of incrementally building training data using both neural and memory-based taggers, together with the Pyrrha annotation tool is highly efficient and yields accurate results. We propose that this simple and effective method could easily be extended to other low-resource historical languages for which no NLP tools exist yet.