Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
In this paper, we propose a method for removing linguistic information from speech for the purpose of isolating paralinguistic indicators of affect. The immediate utility of this method lies in clinical tests of sensitivity to vocal affect that are not confounded by language, which is impaired in a variety of clinical populations. The method is based on simultaneous recordings of speech audio and electroglotto-graphic (EGG) signals. The speech audio signal is used to estimate the average vocal tract filter response and amplitude envelop. The EGG signal supplies a direct correlate of voice source activity that is mostly independent of phonetic articulation. These signals are used to create a third signal designed to capture as much paralinguistic information from the vocal production system as possible-maximizing the retention of bioacoustic cues to affect-while eliminating phonetic cues to verbal meaning. To evaluate the success of this method, we studied the perception of corresponding speech audio and transformed EGG signals in an affect rating experiment with online listeners. The results show a high degree of similarity in the perceived affect of matched signals, indicating that our method is effective.
An analysis of robots (simulators) in education is provided. Promising directions for their development are highlighted, such as realism, interactivity, adaptation and personalization. The features of using simulators in dentistry are considered. The main disadvantages of existing simulators in dentistry have been identified, namely the lack of a communicative component and imitation of patient behavior. The anthropomorphic dental simulator is based on the Robo-C robot, which is a unique combination of advanced technologies and human facial expressions, which allows it to communicate with people, reproduce movements of different parts of the body and express emotions. As dental components, the following components were created and implemented into the Robo-C control system: a Smart jaw, including cameras and a temperature sensor, and a Smart tooth, including a pressure sensor. The Robo-C control system has been upgraded taking into account the Smart jaw and Smart tooth, which made it possible to connect the dental treatment process with the robot’s servos through its linguistic base. The process of analyzing data obtained from Smart jaw cameras using a neural network is described. A two-stage classification scheme for dental defects has been proposed and its effectiveness has been proven. The linguistic base contains a set of rules with the help of which devices (microphone, speakers, servos, Smart jaw, Smart tooth) interact with each other. An example of compiling a linguistic database rule is given. The linguistic base, Smart-jaw and Smart-tooth are configured for one of four cases: caries treatment, tooth preparation for a crown, tooth extraction, endodontic treatment. Treatment quality control is carried out using a comprehensive assessment of communication interaction with the robot and analysis of Smart-jaw and Smart-tooth data. An example of work in one of the cases is given. The anthropomorphic dental simulator presented in the article allows the use of new technologies in the training of dentists, as well as the simulation of various dental procedures, which will significantly improve the practical preparation of students for working with patients.
The purpose of the article is to consider the morphological peculiarities of the system of the nouns in New Bulgarian translation of the “Catechismos” written by Theodore the Studite, which is a part of the manuscripts no. 1/154 kept in Odessa National Scientific Library. The subject of the research is the morphological specifics of nouns in Odessa copy of the “Catechismos” dating from the 18th. The morphological peculiarities of nouns is considered in the context of the formation of a linguistic norm, which allowed the combination with different intensity of linguistic means of several language systems functioning at the time (traditional Middle Bulgarian written language, Church Slavonic Eastern recensions, and vernacular language form). The analysis proposed in this paper presents the extensive system of cases, which does not reflect the real vernacular Bulgarian speech in the 18th; the specifics of the functioning of the gramemes of the case paradigm of masculine, feminine and neuter nouns in the singular and plural forms is analyzed. Usage case endings mistakes, which indicates their artificial nature, are considered. The lack of article of nominal parts of speech is noted; the predominance of compound declension form of the adjectives and participles over short forms is revealed; the relatively high frequency of use of active present participles is registered. The results of the study make it possible to outline some probable factors that determine the writer’s preference for using the linguistic tools of the so-called “bookish”, “traditional”, “archaic” writing systems. An another reason which to some extent explains he usage of case inflections in the text of this relatively late stage of the historical development of the Bulgarian language might be the use of East Slavic copies of the Studite’s sermons by the scriber. Еhe comparison of “Catechismos” copies of South and East Slavic origin is necessary for verification of this assumption, in which we see prospects for further research.
Embodied cognition research identifies mechanisms by which our cognitive activity is connected to body experiences. This approach encompasses not only experimental manipulations but also the quantification of variables related to group and individual differences, i.e., participant-related variables. Moreover, stimuli-related characteristics, such as sensorimotor word ratings, can either be used for the selection of experimental materials or can be the main output of a study themselves. This quantitative information about individuals or stimuli can be collected through non-experimental methods, such as questionnaires and cognitive tests. This chapter gives an overview of questionnaires and cognitive tests often used in embodied cognition research. A questionnaire is a list of questions asking participants to provide information on certain aspects, such as their sociodemographic or medical status. A test is a series of tasks which participants perform for further evaluation by researchers, such as tests of mathematical ability, reading speed, or counting direction. Rating studies collect subjective evaluations of various parameters, typically for large sets of items. The present chapter is divided into two main sections: Participant-related variables and stimuli-related characteristics. We present examples from cognitive linguistics, psycholinguistics, psychophysics, as well as from research on numerical cognition, peripersonal space, and attitudes towards social robots.
The goal of this contribution is to present The Digital Rosetta Stone, which is a project developed at Leipzig University by the Chair of Digital Humanities and the Egyptological Institute/Egyptian Museum Georg Steindorff in collaboration with the British Museum and the Digital Epigraphy and Archaeology Project at the University of Florida. The aims of the project are to produce a collaborative digital edition of the Rosetta Stone, address standardization and customization issues for the scholarly community, create data that can be used by students to understand the language and content of the document, and produce a high-resolution 3D model of the stone. First, the three versions of the text were transcribed and encoded in XML according to the EpiDoc guidelines. Next, the versions were aligned with the Ugarit iAligner tool that supports the alignment of ancient texts with modern languages, such as English and German. All three texts were then parsed syntactically and morphologically through Treebank annotation. Finally, the project explored new 3D-digitization techniques of the Rosetta Stone in the British Museum in order to enhance traditional archaeological methods and facilitate the study of the artifact. The results of this work were used in different courses in Digital Humanities, Digital Philology, and Egyptology.
- output-{ciep,treebanks}-full.csv: frequency and entropy for all the categories, using four types of combinations of layers;<br> - plots.R: R script to draw plots from the output files;<br> - readReport-{CIEP+,treebanks}.R: R script to extract frequency and compute entropy from the report files (not included);<br> - ud-wordorder.py: Python script to extract word order pairs from conllu files and write them in report files. Unfortunately, I cannot include the report files, as CIEP+ is protected by copyright; the analysis can be however replicated with respect to the UD Treebanks.
Introduction A study was conducted to investigate if an individual’s trust in law enforcement affects their perception of the emotional facial expressions displayed by police officers. Methods The study invited 77 participants to rate the valence of 360 face images. Images featured individuals without headgear (condition 1), or with a baseball cap (condition 2) or police hat (condition 3) digitally added to the original photograph. The images were balanced across sex, race/ethnicity (Asian, African American, Latine, and Caucasian), and facial expression (Happy, Neutral, and Angry). After rating the facial expressions, respondents completed a survey about their attitudes toward the police. Results The results showed that, on average, valence ratings for “Angry” faces were similar across all experimental conditions. However, a closer examination revealed that faces with police hats were perceived as angrier compared to the control conditions (those with no hat and those with a baseball cap) by individuals who held negative views of the police. Conversely, participants with positive attitudes toward the police perceived faces with police hats as less angry compared to the control condition. This correlation was highly significant for angry faces ( p &lt; 0.01), and stronger in response to male faces compared to female faces but was not significant for neutral or happy faces. Discussion The study emphasizes the substantial role of attitudes in shaping social perception, particularly within the context of law enforcement.
Atuy Galon is a unique fanfiction that began as a series of short stories on social media, eventually gaining prominence and being published in a comic strip format. What sets this work apart is its distinctive use of slang language, an ever-evolving linguistic phenomenon that resonates particularly with younger audiences. The central figures in the narrative, Atuy, Anton, and Sahrul, not only use slang but each has developed their own distinct slang lexicon, reflecting varied facets of cultural identity. As language remains paramount in shaping one's cultural self-awareness, this research meticulously examines the linguistic choices of these characters. Employing a descriptive qualitative approach alongside a rigorous discourse analysis methodology, the study aims to decode the linguistic intricacies within Atuy Galon and their broader implications for cultural identity formation. The significance of this exploration extends beyond literary analysis; it offers a window into the dynamic interplay between language, culture, and identity among today's youth. Additionally, it underscores the transformative power of technology in reshaping linguistic norms and practices.
Introduction: Effective communication often involves expressing disagreement while maintaining social harmony, which is influenced by cultural and linguistic norms. Native speakers of English typically employ various politeness strategies in their disagreement speech acts. However, Iraqi EFL learners may navigate these strategies differently due to variations in cultural norms and language proficiency. Therefore, the current study aimed to contrastively analyze the way Iraqi EFL learners and native English speakers perform the speech act of disagreement in light of politeness. Methodology: In this regard, a discourse completion test (DCT) was administered to 66 participants, comprising 33 Iraqi EFL students studying English as a foreign language (TEFL) and 33 native English speakers. The DCT was made up of scenarios that mirrored real-life circumstances in order to provoke responses from people who disagreed with them. Brown and Levinson’s (1987) theory of politeness was employed to analyze participants’ utterances. Results: The findings indicated that while expressing disagreement with people of higher, participants in both groups were more concerned with keeping their interlocutors’ positive faces. Furthermore, the study findings indicated that despite differences in the two groups of participants, Iraqi EFL learners utilized positive indirect politeness strategies more frequently than English native speakers. On the other hand, English native speakers applied direct and negative politeness strategies. Conclusion: Generally, the findings indicated that both groups tended to use the most direct type of disagreement as the social distance and power relation decreased.
Dialogue-level dependency parsing has received insufficient attention, especially for Chinese. To this end, we draw on ideas from syntactic dependency and rhetorical structure theory (RST), developing a high-quality human-annotated corpus, which contains 850 dialogues and 199,803 dependencies. Considering that such tasks suffer from high annotation costs, we investigate zero-shot and few-shot scenarios. Based on an existing syntactic treebank, we adopt a signal-based method to transform seen syntactic dependencies into unseen ones between elementary discourse units (EDUs), where the signals are detected by masked language modeling. Besides, we apply single-view and multi-view data selection to access reliable pseudo-labeled instances. Experimental results show the effectiveness of these baselines. Moreover, we discuss several crucial points about our dataset and approach.
This paper introduces CORAE, a novel web-based open-source tool for COntinuous Retrospective Affect Evaluation, designed to capture continuous affect data about interpersonal perceptions in dyadic interactions. Grounded in behavioral ecology perspectives of emotion, this approach replaces valence as the relevant rating dimension with approach and withdrawal, reflecting the degree to which behavior is perceived as increasing or decreasing social distance. We conducted a study to experimentally validate the efficacy of our platform with 24 participants. The tool’s effectiveness was tested in the context of dyadic negotiation, revealing insights about how interpersonal dynamics evolve over time. We find that the continuous affect rating method is consistent with individuals’ perception of the overall interaction. This paper contributes to the growing body of research on affective computing and offers a valuable tool for researchers interested in investigating the temporal dynamics of affect and emotion in social interactions.
Moral decision-making is influenced by various factors, including personality and language. In this cross-sectional study, we investigated the Foreign-Language effect (FLe) in early, highly proficient, Catalan-Spanish bilinguals and examined the role of several personality dimensions in their responses to moral dilemmas. We obtained a multilevel data structure with 766 valid trials from 52 Catalan-dominant undergraduate students who read and responded anonymously to a computerized task with 16 standardized moral dilemmas, half in Catalan and half in Spanish. Results of a multilevel multivariate logistic regression analysis showed that consistent with previous research, participants gave more utilitarian responses to impersonal than personal dilemmas. The language of the dilemma had no effect on the response (dichotomous: utilitarian vs. deontological), decision time, or affective ratings, contradicting the hypothesis of shallower emotional processing of the information in the second language. Interestingly, cruelty features of psychopathy were significantly associated with an enhanced proportion of utilitarian decisions irrespective of the language or the nature of the dilemmas. Furthermore, cruelty features interacted with participants' assessment of dilemma aspects like vividness and verisimilitude. Overall, our findings suggest that early bilinguals immersed in a dual-language context using close Romance languages do not show the FLe and that personality traits like cruelty can modulate moral decisions regardless of language or dilemma type.
It is widely known that English teaching practices are largely rooted in colonialism and linguistic imperialism – the belief in the superiority of the Western teaching methods and linguistic norms established in such countries as the UK and the US. The current trend to decolonize English teaching has been gaining momentum for over three decades but has not entirely penetrated into the mainstream teaching of English, with English for Academic Purposes not being an exception. Relatively little focus has been placed in the research onto non-English speaking contexts with the purpose of analysing the current state of EAP provision at universities and gauging the influence of the native-speakerist academic English norms on EAP students’ writing. In the case of the EU context, it may be particularly pertinent to investigate the potential influence that Brexit might have had in terms of rejecting the imposition of the Standard British English norms and associated teaching approaches. This paper is meant to be a reflection with an attempt to stimulate the discussion of EAP teaching practices and academic discourses in the EU higher education in the post-Brexit era. It will consider the issues in the EAP provision in the EU with the example of Portuguese HE and will reflect on the native-speakerist tendencies within the academia and ways to tackle the dominance of the Anglophone norms. This paper hopes to contribute to the argument in favor of the decolonization of EAP teaching practices in non-English speaking contexts, as decolonization can help foster a more equitable and inclusive world.
Objective: Insomnia affects 30-45% of the world population, is related to mortality (i.e., auto accidents and job-related accidents), and is related to mood and affect disorders such as anxiety and depression. Better understanding of insomnia via increased research will decrease the burden on insomnia. The neurocognitive model of sleep proposes that conditioned somatic and cognitive hyperarousal develop in response to repeated pairings of sleep-related stimuli with insomnia-related wakefulness. The purpose of this study was to examine the neurocognitive model of sleep using a novel laboratory paradigm, the Sleep Approach Avoidance Task (SAAT). It was hypothesized that individuals who report symptoms of insomnia will display a bias for negative sleep-related images from the SAAT, which is presumably a reflection of cognitive, behavioral and physiological processes associated with hyperarousal. It was also hypothesized that participants who report poor sleep would provide different subjective ratings for negative images (i.e., stronger valence and arousal) than individuals who reported better sleep. Participants and Methods: An initial sample of 66 healthy college-aged participants completed the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI) the Dysfunctional Attitudes and Beliefs about Sleep (DBAS) scale and the Epworth Sleepiness Scale (ESS). Participants also completed the SAAT. The SAAT was developed to assess sleep-related bias in adults. The SAAT is a visual, joystick controlled reaction time task that measures implicit bias for positive and negative sleep-related images. At the end of the task the participants are also asked to rate each image along three dimensions included valence, arousal and dominance. Results: There was a positive correlation between the SAAT and the ISI [r(61) =.30, p =.01], indicating that symptoms of insomnia are related to negative approach-related bias for sleep-related images. No other correlations were observed between the SAAT and self-report sleep measures. With regard to rating of images, higher dominance ratings for negative images were correlated with the SAAT [r(62) =.24, p =.03], which indicates that the approach bias for negative images is associated with “being in control.” Multiple linear regression was used to test if ISI scores and dominance ratings for negative images significantly predicted SAAT bias scores. The overall regression was statistically significant [r2 =.13, F(2, 58) = 4.15, p =.02]. ISI scores significantly predicted SAAT scores (ß =.27, p =.04), whereas dominance ratings for negative images did not significantly predict SAAT scores (ß =.20, p =.11). Exploratory correlational analyses were also completed for ratings of images and other sleep self-report measures. Valence ratings for positive sleep-related images were positively correlated with the ESS [r(64) =.36, p =.01], whereas valence ratings for negative sleep-related images were negatively correlated with the ESS [r(64) = -.24, p =.03]. Conclusions: Hypotheses were partially supported with the ISI being the only self-report measure associated with negative bias for sleep-related images. While ratings of dominance are associated with bias for negative sleep-related images, these ratings do not provide unique variance. These findings indicate a cognitive processing bias for sleep-related stimuli among young adult poor sleepers. Limitations, implications for assessment and intervention are discussed.
GUARDIAN-MT (GUarded by Advanced Radar technology-based Diagnostics Applied in palliative and intensive care Nursing – Music Therapy) was the music therapy subproject within a research examining the reliability of non-contact vital parameter measurements using radar, compared to validated measurement methods. The focus of GUARDIAN-MT was on evaluating live-played sounds during a cold-pressor induced pain stimulus using a mixed-method approach. In addition to direct standard measures, a content-analytical procedure was applied to analyze telephone interviews regarding the retrospective experience of the pain event according to psychologically relevant experiential categories. It was found that particularly the arousal ratings, assessed by independent evaluators, had a substantial influence on the perceived valence of the pain event. Arousal-reducing interventions, such as well-coordinated performance of individual live music, can contribute to experiencing the pain not only as milder, but also less prolonged. The analysis of qualitative data can help uncover and deepen the essential qualities and effects of music therapy interventions.
Mornings are salient times for disrupted affect that may be impacted by prior sleep. The current study extends work linking sleep disruptions with negative affect by examining how nightly changes in sleep duration, timing, and quality relative to a person's average impact morning affect. We further tested whether depression severity moderated the relationship between nightly variations in sleep and morning affect. This is a secondary analysis of participants ages 18-65 years with varying levels of depression (N = 91) who wore an Actiwatch for 3-17 days (n = 73) while reporting morning affect using a visual analogue scale. Multilevel models tested the previous night's sleep duration, timing, or quality as a predictor of morning affect. Sleep measures were group-mean centred to account for nightly variation in participants' sleep. A cross-level interaction between depression severity and nightly sleep was entered. Sleeping longer (b = 0.1; p < 0.001) and later (b = 1.8; p = 0.01) than usual were both associated with better morning mood. There was a significant interaction between nightly actigraphic sleep duration and depression severity on morning affect (b = 0.003; p = 0.003). Participants with higher depression severity reported worse affect upon waking after sleeping less than their usual. In comparison, sleeping less than usual did not affect morning affect ratings for participants with lower depression. A similar interaction was found for sleep quality (b = 0.02; p < 0.001). There was no interaction for midsleep timing. Sleeping less than usual impacted morning affect in individuals with greater depression, potentially suggesting a pathway by which sleep disturbances perpetuate depression.
Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs’ powerful ability in learning expressive graph representations. However, most of these works learn graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone, and the errors introduced in the static graph cannot be corrected and might accumulate in later stages, and (2) the graph construction stage and graph representation learning stage are disjoined, which negatively affects the model’s running speed. In this paper, we propose a joint-learning-based dynamic graph learning framework and apply it to two typical structured prediction tasks: syntactic dependency parsing, which aims to predict a labeled tree, and semantic dependency parsing, which aims to predict a labeled graph, for jointly learning the graph structure and graph representations. Experiments are conducted on four datasets: the Universal Dependencies 2.2, the Chinese Treebank 5.1, the English Penn Treebank 3.0 in 13 languages for syntactic dependency parsing, and the SemEval-2015 Task 18 dataset in three languages for semantic dependency parsing. The experimental results show that our best-performing model achieves a new state-of-the-art performance on most language sets of syntactic dependency and semantic dependency parsing. In addition, our model also has an advantage in running speed over the static graph-based learning model. The outstanding performance demonstrates the effectiveness of the proposed framework in structured prediction.
Social touch is crucial for human well-being, as a lack of tactile interactions increases anxiety, loneliness and need for social support. To address the detrimental effects of social isolation, we build on cutting-edge research on social touch and movement sonification to investigate whether social tactile gestures could be perceived through sounds, a sensory channel giving access to remote information. Four experiments investigated participants’ perception of auditory stimuli that were recorded with our ‘audio-touch’ sonification technique, which captures the sounds of touch. In the first experiment, participants correctly categorized sonified skin-on-skin tactile gestures (i.e., stroking, rubbing, tapping, hitting). In the second experiment, the audio-touch sample consisted of the sonification of six socio-emotional intentions conveyed through touch (i.e., anger, attention, fear, joy, love, sympathy). Participants’ valence ratings of the underlying touch were coherent with the intended emotions, while their categorization presented more variability. In two additional experiments investigating the characteristics of the surface involved in the tactile interactions (i.e skin or object), skin proved to be a critical factor in the auditory categorization of both gestural and emotional audio-touch stimuli. This result reveals that human skin-on-skin interactions convey information which sets them apart, perceptually, from object-on-object interactions. Our research thus unveils that social touch can be perceived through sounds, when they are obtained with our specific sonifying methodology, tailored for skin-on-skin interactions. This bears great promise for giving remote access, through the auditory channel, to meaningful social touch interactions.
This corpus-based study aimed to investigate the presence of context-dependent linguistic errors in a corpus of clinical reports. The data were taken from a corpus comprising more than 2 million words and made up of clinical reports from emergency medicine, intensive care unit, general surgery, and psychiatry. Quantitative and qualitative analyses were carried out. A language model based on n-grams was developed for the detection of errors, parameters for the selection of cases were defined, and a classification tool was implemented. The findings indicated that emergency medicine was the medical specialty with the highest number of context-dependent errors and that the most frequent type of error was omission of written accent. Furthermore, the analysis revealed the presence of errors of competence due to the incorrect application of the linguistic norm of Spanish, phenomena of phonetic similarity, and composition of words; it is also worth noting that performance errors occurred due to rapid typing on the keyboard. This study constituted the first analysis and creation of a typology of context-dependent errors for the medical domain in Spanish. It contributed to the design of a module based on linguistic knowledge that can be used for the development and improvement of automatic correction systems that, in turn, are used for data processing in medicine.
The article is devoted to the study of the texts of the museum exhibition, which play the mediator role between visitors of a museum and its artifacts. The purpose of the study is to consider the text of the museum exhibition as an effective tool for intercultural communication. The review of foreign and domestic works shows that multilingual museum exposition helps to develop a dialogue and understanding between different cultures. The author studied the museum labels of the State Hermitage Museum, their grammatical and stylistic peculiarities, which should be taken into account while translating museum texts from Russian into English. It is determined that the grammatical peculiarities of the museum texts are verbless constructions, incomplete sentences, where linguistic norms are violated. The style of the texts of museum expositions is, as a rule, publicistic, some texts also have features of scientific, spoken and literary styles. While translating, the translator should aim to save the meaning and functional style.
BACKGROUND AND OBJECTIVES: Previous research identified cognitive reappraisal as an adaptive emotion regulation strategy. However, theories on emotion regulation flexibility suggest that reappraisal effectiveness (RE) may depend on an individual's familiarity with stressors. In this study, we expect high reappraisal inventiveness (RI), i.e., the generation of many and categorically different reappraisals, to increase RE for individuals with low situational familiarity. Individuals with high situational familiarity, however, would be more effective with low RI. DESIGN: A total of 148 participants completed the Script-based Reappraisal Task, in which they were presented with fear- and anger-eliciting scripts. Depending on trial type, participants were instructed to reappraise (reappraisal-trial) or react naturally (control-trial) to the scripts. After each trial, participants indicated affective states and reappraisals. We assessed RI and calculated RE-scores as difference between affect ratings in reappraisal- and control-trials for valence and arousal. Finally, participants rated the familiarity with each situation. RESULTS: The results indicated a significant moderating effect of situational familiarity on the relationship between RI and RE-valence (not RE-arousal). The moderation was mainly driven by a detrimental effect of RI for individuals with high situational familiarity. CONCLUSIONS: Our results hint at the importance of individual experience with emotional content in the research of cognitive reappraisal.
Genre classification for text documents is useful in media monitoring and detection of misinformation. Recent work in text classification for genre has shown that advanced algorithms such as neural networks and transformers are well-suited for the purpose. However, for shorter text documents, such as those obtained from social media or news articles, training of deep learning models becomes challenging since they require a large amount of input. Furthermore, genre classification of text summaries, such as headlines of news, is an important direction which has not been explored at large. In this work, the effect of Extractive and Abstractive Summarization on classification for genre of text documents was evaluated. Gensim summarizer was used to obtain extractive summaries and the Pegasus summarizer to obtain abstractive summaries. For classification, two classes of genres, Fiction and Non-fiction, were considered while the gold standard Brown Corpus was used for experimentation. The features used for genre classification were frequencies of various Part-of-Speech (PoS) tags derived from five Penn TreeBank annotated tags. Logistic Regression (LR) and Support Vector Machines (SVM) were used for classification purposes. The results of classification were better for summaries obtained using the extractive technique, indicating that the features of extractive summaries remain in agreement with the documents from which the summary is constructed as compared to abstractive summaries. Further, the SVM classifier performed better than the LR classifier. For exhaustive coverage of the research goal, further experimentation with the number of words of the output summaries of the extractive technique was performed to arrive at a threshold value of the length of summaries. The value indicated that summaries as short as 80 words can be successfully classified using this method.
Genres such as indie (Beal 2009) and hip-hop (Eberhardt & Freeman 2015) feature dialectal traits in English, but whether genres form targets distinct from speech remains unclear. We examine genre effects on phonetic variation in Quebec French music by probing the role of genres (pop, country, alternative, and indie) on laxing and diphthongization, processes characteristic of Quebec French (Walker 1984). Stigma facing formal varieties of Quebec French has vanished within dialect (Kircher 2012), yet remains for processes that vary regionally or socioeconomically (Côté 2012; Côté & Lancien, 2019). Whereas laxing is categorical and non-stigmatized (Côté 2012; Paradis & Dolbec, 1998), diphthongization is variable and stigmatized (Côté 2012). We use a novel corpus of ten Québécois singers who released multiple albums from 2011-2021 (29 albums; 326 songs). We find the emergence of genre-specific linguistic norms distinct from speech and argue that genres in music parallel sociolects.
Research into cultural tastes has commonly sought to analyze and understand preferences in terms of notions of familiarity. Such approaches are inadequate, however, when it comes to examining our engagement with unfamiliar cultural content. This paper responds to this gap by examining how people respond to algorithmic recommendations of culture through a case study of unfamiliar Australian art music. It firstly identifies three different “techniques’ by which audiences engage with and value music: functional, emotional, and intellectual. The analysis then examines how these techniques, together with measures of familiarity and the acoustic “materiality” of the music itself, combine to predict the affective ratings given to music recommendations. The findings show that audiences display a surprising capacity to engage with the unfamiliar. The paper argues for the need to develop more nuanced understandings of the relationship between familiarity and preferences which are capable of accommodating a taste for the unfamiliar.
This study aims to evaluate and provide recommendations by testing the usability level of the Odoo Operation & Maintenance ERP Module application. Using the SUS Questionnaire method, the results of usability testing were analyzed into each parameter of the SUS Questionnaire. The SUS questionnaire has an assessment criteria of “Unacceptable category” with a value range of 0–50 class F and has an adjective rating of “Worst Imaginable to Poor”, the category “Low Marginal” with a score range of 51-62.5 class F and has a word rating OK trait, Marginal High Category with a range of 62.6–70 grade D and adjective rating OK, and Acceptable Category with a score range of 70–100 grade C to A and adjective rating good, very good to best. The test results get a value of 53.25 including the OK Marginal Low category, meaning that it needs improvement so that the system can be well received by users. Further interviews were also conducted with ten informants to obtain a specific description of the complaints of system users. From the results of the interview, an analysis was carried out to provide evaluations and recommendations for improvement of the Odoo ERP Operation & Maintenance Module.
This article proposes to look at the theory of translanguaging from the angle of its universality/non-universality, bringing into discussion a new empirical case - speech strategies and practices of highly educated multilingual migrants from the former USSR and multilingual residents of modern Russia. The authors conclude that people whose socialization took place in this region are characterized by rigid ideas about the language norm and authenticity, about the “purity” of languages and high standards of speaking them. These concepts apply to all languages, both native and foreign. Traditional and conservative language ideologies are widespread in the Soviet and post-Soviet area, which is the reason why translanguaging is often perceived as careless speech, incomplete language competence. In this sense, this regional case demonstrates the non-universality of translanguaging theory and the importance of contexts in which attitudes towards multilingualism, linguistic norm and related linguistic phenomena are formed. It is primarily the western democratic, postcolonial context of translanguaging practice and theory that explains why the language attitudes and speech behavior of residents of (post)Soviet region do not fit into it.
In this work, we introduce a framework that unifies existing implementations for the tasks of constituent and dependency parsing as sequence labeling problems. The system provides a way to encode both formalisms as sequences of one label per word, so they can be used with any existing general-purpose sequence labeling architecture. More particu- larly, we implement three linearizations to encode constituent trees and four linearizations for dependency trees. All encoding functions ensure completeness and injectivity. We will also train a sequence labeling neural system to learn such encodings, and compare their ef- fectiveness on standard constituent (PTB and SPMRL treebanks) and dependency parsing (a subset of treebanks from the UD collection) evaluation frameworks.
This paper offers the basic guidelines of a formalized version of the Lexical Constructional Model (LCM; Ruiz de Mendoza & Mairal Usón, 2008, 2011; Ruiz de Mendoza & Galera, 2014), the Formalized Lexical-Constructional Grammar (FL_CxG), which will pave the way for future computational developments, such as parsers or lexical databases. The FL_CxG deploys (i) the typologically oriented syntactic apparatus of Role and Reference Grammar (Van Valin, 2005; Van Valin & LaPolla, 1997), (ii) the catalogue of constructional units arranged in a 4-layer typology, as proposed by the LCM, and (iii) some insights for semantic representations from the Generative Lexicon Theory (Pustejovsky, 1995; Pustejovsky & Batiukova, 2019), and Minimal English (Goddard, 2018). All the components of the FL_CxG (lexical units and construct(ion)s) are formally encoded as Typed Feature Structures in the format of Attribute Value Matrixes. These units are to be understood as constraints operating in the unification processes which underlie the generation/decoding of a given fragment of language.
This work investigates the effect of grid-connected converter topology on equivalent converter output impedance with a specific focus on the diagonal dominance of the impedance matrix across a frequency range. When considering multiple-input multiple-output systems most traditional stability techniques are reliant on the diagonal dominance of the studied system. Therefore, a rating of diagonal dominance is proposed based upon the correlation coefficient between row and column in the impedance matrix. This provides a scale that ranges from off-diagonally dominant (-1) through uniformly distributed (0) and up to diagonally dominant (1) across a range of frequencies. The scale is used to specify which control structures can be considered as diagonally dominant at certain frequencies and which control components have the greatest effect on the rating. A direct relation is found between system exhibiting a diagonal dominance rating of 0.7 and above and the efficacy of traditional stability margins. Traditionally strong systems with low network impedance where controllers can be tuned conservatively exhibit high degrees of diagonal dominance and can be analysed quickly with traditional margins with minimal error. For systems exhibiting a lower rating, disk margins are explored as an alternative which offer greater accuracy. Additionally, more realistic perturbations of gain and phase occurring simultaneously in multiple channels can be considered which is more applicable for the modern electricity network with a high penetration of grid-connected converters.
This work delves into the semantics of Old English lexical paradigms based on strong verbs. Its aim is to describe the patterns of semantic inheritance that hold in these paradigms, which present morphologically related words sharing the form and meaning of the base of derivation. The analysis carried out permits to circumscribe semantic derivation in these Old English lexical paradigms into the lexical entailment relations of troponymy, -troponymy, backward presupposition and cause, and the semantic relations of synonymy and opposition. The data of research has been retrieved from the lexical database Nerthus (Martín Arista et. al 2016). On the theoretical side, this examination follows the English lexical database WordNet (Princeton, 2010). This research unfolds a systematic methodology that is thoroughly described and illustrated by means of the paradigm (ge)berstan. It has not only been possible to circumscribe semantic derivation into the six semantic relations mentioned, but also to determine their frequency of occurrence in the paradigms under analysis, which indicates that synonymy and troponymy are the most recurrent semantic relations.
Emotion measurement is crucial to conducting emotion research. Numerous studies have extensively employed textual scales for psychological and organizational behavior research. However, emotions are transient states of organisms with relatively short duration, some insurmountable limitations of textual scales have been reported, including low reliability for single measurement or susceptibility to learning effects for multiple repeated use. In the present article, we introduce the Highly Dynamic and Reusable Picture-based Scale (HDRPS), which was randomly generated based on 3,386 realistic, high-quality photographs that are divided into five categories (people, animals, plants, objects, and scenes). Affective ratings of the photographs were gathered from 14 experts and 209 professional judges. The HDRPS was validated using the Self-Assessment Manikin and the PANAS by 751 participants. With an accuracy of 89.73%, this new tool allows researchers to measure individual emotions continuously for their research. The non-commercial use of the HDRPS system can be freely accessible by request at http://syy.imagesoft.cc:8989/Pictures.7z. HDRPS is used for non-commercial academic research only. As some of the images are collected through the open network, it is difficult to trace the source, so please contact the author if there are any copyright issues.
Muitas organizações têm dificuldade em recuperar e extrair informações dos seus repositórios de documentos técnicos, em especial operadoras de óleo e gás que há várias décadas acumulam relatórios e documentos geocientíficos. No entanto, a maior parte dos recursos linguísticos para o processamento de linguagem natural é extraída de páginas da internet em inglês. Neste artigo, apresentamos os recursos linguísticos desenvolvidos ao longo do projeto Petrolês, com ênfase no PetroNer, corpus padrão ouro anotado com entidades do domínio, dependências sintáticas, e alinhado a uma ontologia de conceitos geológicos. Relatamos o processo de construção do PetroGold, treebank padrão ouro usado na geração de um modelo customizado para anotação de dependências sintáticas, e detalhamos o processo de anotação de entidades no PetroNer, realizado por meio de regras. Também realizamos um estudo sobre a aplicação das regras no corpus e, por fim, descrevemos características linguísticas do material que compõe o Petrolês, comparando-o com um corpus de textos jornalísticos.
Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.
The specificities of Arabic parsing, such as agglutination, vocalization, and the relatively order-free words in Arabic sentences, remain major issues to consider. To promote its robustness, such parseing should define different types of constraints. Property Grammar (PG) formalism verifies the satisfiability of the constraints directly on the units of the structure, thanks to its properties (or relations). In this context, we propose to build a probabilistic parser with syntactic properties, using a PG, and we measure the production rules in terms of different implicit information and in particular the syntactic properties. We experimented with our parser on the treebank ATB, using the parsing algorithm CYK, and we obtained encouraging results. Our method is also automatic for implementation of most property types. Its generalization for other languages or corpus domains (using treebanks) could be a good perspective. Its combination with pre-trained models of BERT may also make our parser faster.
One of the most crucial Natural Language Processing (NLP) tasks is associated with the universality-driven development of language resources for different languages (e.g. Universal Dependencies (UD), UniMorph, PARSEME, etc.). This article describes the possibility of creating a Syntactic TreeBank for Georgian, and consists of four sections. The first section briefly describes different linguistic resources that exist concerning Georgian and mentions the importance of syntactic annotation. The second section focuses on the tools used for mapping the existing targets for Georgian to the UD format. The third section includes a description of the principles of syntactic annotation and language-specific documentation files, while the fourth section summarizes the work done and describes the future stages of the development of the syntactic TreeBank.
Linguistic heterogeneity and fluidity – prominently captured in the notion of ‘translanguaging’– are starting to be seen as normative and natural. In turn, homogeneity and fixity, instantiated for example in standard languages, are becoming the ‘odd-ones-out.’ I challenge the dichotomy between linguistic fluidity (languaging) and fixity (named languages), in a situated conceptual account of translingual writing practices in English classrooms in a Khayelitshan primary school. These spaces fold the linguistic fluidity typical of South African townships, and the fixity of two standard languages, into one complex spatial repertoire. Operationalizing this spatial perspective, I suggest that students are constantly engaged in relanguaging, recursively sorting out the classroom repertoire according to the various linguistic norms enfolded in the space, and of bringing together linguistic resources in various combinations. Relanguaging systematically unsettles the dichotomy between fluid languaging and fix institutional language norms retained in dominant conceptualizations of translanguaging. This way it opens up new conceptual and analytical perspectives with possible pedagogical implication for writing instruction and testing. Standard English could, for example, be assessed beyond its own confines, using writing tasks that can make visible increasingly sophisticated linguistic sorting skills as students.
This article delves into the literary canon, a concept shaped by social biases and influenced by successive receptions. The canonization process is a multifaceted phenomenon, emerging from the intricate interplay of sociological, economic, and political factors. Our objective is to detect the underlying textual dynamics that grant certain works exceptional longevity while jeopardizing the transmission of the majority. Drawing on various criteria, we present an operational framework for defining the French literary canon, centered on its contemporary reception and emphasizing the role of institutions, particularly schools, in its formation. Leveraging natural language processing and machine learning techniques, we unveil an intrinsic norm inherent to the literary canon. Through statistical modeling, we achieve predictive outcomes with accuracy ranging from 70% to 74%, contingent on the chosen scale of canonicity. We believe that these findings detect what Charles Altieri calls a “cultural grammar”, referring to the idea that canonical works in literature serve as foundational texts that shape the norms, values, and conventions of a particular cultural tradition. We posit that this linguistic norm arises from biased latent selection mechanisms linked to the role of the educational system in the canon-formation process.
Humanlike androids can function as social agents in social situations and in experimental research. While some androids can imitate facial emotion expressions, it is unclear whether their expressions tap the same processing mechanisms utilized in human expression processing, for example configural processing. In this study, the effects of global inversion and asynchrony between facial features as configuration manipulations were compared in android and human dynamic emotion expressions. Seventy-five participants rated (1) angry and happy emotion recognition and (2) arousal and valence ratings of upright or inverted, synchronous or asynchronous, android or human agent dynamic emotion expressions. Asynchrony in dynamic expressions significantly decreased all ratings (except valence in angry expressions) in all human expressions, but did not affect android expressions. Inversion did not affect any measures regardless of agent type. These results suggest that dynamic facial expressions are processed in a synchrony-based configural manner for humans, but not for androids.
Abstract Proposals such as continuity and causality-by-default relate the level of expectedness of a relation to its linguistic marking as an explicit or implicit relation. We investigate these two proposals with regard to the English transcripts of six TED Talks and their Lithuanian, Portuguese and Turkish translations in the TED-Multilingual Discourse Bank (TED-MDB), annotated for discourse relations, following the Penn Discourse Treebank style of annotation. Our data shows that the discontinuous relations contrast and concession are indeed frequently explicit in all languages. But continuous relations show differences per relation and language. For instance, cause is frequently conveyed implicitly in English and Portuguese, but not in Lithuanian and Turkish. We explore temporal continuity by analysing whether the forward-order sense result is more frequently implicit than the backward-order reason. The hypothesis is confirmed by English and Portuguese, but not Lithuanian and Turkish. However, in Turkish, the arguments of the backward-order relation reason are frequently presented by the reversed order of arguments, retaining the linear order of events even in the presence of the connective. The causality-by-default hypothesis is not confirmed, as cause is not the most frequent implicit relation in the four languages.
This study explores the nuanced effects of social media on society, emphasising how it has both positive and negative aspects. Positively, social media has revolutionised global connectivity by democratising journalism, encouraging participation across great distances, and giving companies access to low-cost advertising channels. The report does admit many drawbacks, too, such as privacy issues, cyberbullying, and the quick dissemination of false information. The study promotes digital literacy, user education, and proactive actions from social media companies to address these problems. It highlights how critical thinking abilities are necessary to successfully traverse the internet environment. Furthermore, the study draws attention to the linguistic influence of social media by presenting acronyms, abbreviations, and colloquial language, prompting concerns about possible negative effects on written language proficiency and the significance of maintaining linguistic norms. Overall, the study highlights how social media has a significant impact on a variety of fields, including activism, politics, marketing, and education. It also highlights the need for a balanced strategy to maximise social media’s advantages while minimising its drawbacks
The Covid-19 pandemic in the last 3 years has strengthened E-commerce growth, making online shopping the new norm due to restricted offline activities. To aid buyers and sellers in conducting transactions in E-commerce, E-commerce platforms have introduced features like product descriptions, product photos, ratings, and reviews. These features have created a competitive landscape, benefiting sellers who can use them effectively. Nevertheless, many sellers still struggle to optimize these features and market their products effectively to buyers. Failure to optimize these features correctly restricts the marketing strategy's effectiveness and puts sellers at risk for unanticipated difficulties that may be prevented by determining the various effects of these features on customers’ purchase intentions. Therefore, this research aims to analyze the impact of product descriptions, product photos, and ratings & reviews on customers' purchase intention in E-commerce. A quantitative approach is used in this study, where the data is analyzed through descriptive statistics and PLS-SEM. The result of this study suggested that all three features of product description, product photo, and rating & review significantly and positively influence purchase intention in E-commerce. In addition, the author also found that moderation of perceived trust significantly affects product description and rating & review on purchase intention, while the moderation of perceived risk only significantly affects rating & review on purchase intention. The finding of this research is expected to give insights to E-commerce sellers on optimizing the features in E-commerce to increase the customers’ purchase intention.
Automatic syntactic analysis of a sentence is an important computational linguistics task. At present, there are no syntactic structure parsers for Russian that are publicly available and suitable for practical applications. Ground-up creation of such parsers requires building of a treebank annotated according to a given formal grammar, which is quite a cumbersome task. However, since there are several syntactic dependency parsers for Russian, it seems reasonable to employ dependency parsing results for syntactic structure analysis. The article introduces an algorithm that allows to construct the constituency tree of a Russian sentence by a syntactic dependency tree. The formal grammar used by the algorithm is based on the D.E. Rosenthal’s classic reference. The algorithm was evaluated on 300 Russian-language sentences. 200 of them were selected from the aforementioned reference, and 100 from OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. During the evaluation, the sentences were passed to syntactic dependency parsers from Stanza, SpaCy, and Natasha packages, then the resulted dependency trees were processed by the proposed algorithm. The obtained constituency trees were compared with the trees manually annotated by experts in linguistics. The best performance was achieved using the Stanza parser: the constituency parsing F1–score was 0.85, and the sentence parts tagging accuracy was 0.93, that would be sufficient for many practical applications, such as event extraction, information retrieval and sentiment analysis.
Bullying has moved online as a result of the technological revolution, which was previously limited to physical boundaries. One type of cyber bullying is ridicule or insult. According to the report the cyber bullying on social media is getting worse. Insulting words change over time, and the same word can mean different things depending on the situation. A comment cannot be considered bullying simply because it contains such a word. Therefore, simple keyword spotting methods are insufficient for labelling comments. Lexical databases like Word Net, which provide synonyms and homonyms for words, have been utilized in other languages to address this issue. It is difficult to identify a word as bullying because there is no English-language lexical database. As a result, the proposed work solved the problem by following the rules. Outliers were removed from the collection of tweets containing profane language, and the remaining tweets were pre-processed. Five feature extraction rules were used to find insults in the text. The Support Vector Machine (SVM), K-nearest neighbor (KNN), and Naive Bayes algorithms were then utilized. With F1-score of 91 percent, the findings demonstrate that SVM along with an RBF kernel performs better. The fact that this research focuses on English-language cyberbully detection is novel and has not been done before.
This paper introduces a novel graph polynomial approach for differentiating tree structures in dependency grammar. Utilizing this polynomial representation, we develop a metric to assess the similarity in syntax. This approach offers a detailed and inclusive analysis of the dependency structures and relationships in sentence construction. We employ this polynomial method to examine sentence structures across various languages in the Parallel Universal Dependencies treebanks. Our analysis includes comparing the syntax of original sentences and their translated counterparts in diverse languages, alongside a comprehensive study of syntactic typologies within these treebanks. Additionally, we explore the application of our methodology in evaluating the syntactic diversity within language corpora.