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18265 papers
PURPOSE: A long-standing issue in identifying developmental language disorder (DLD) in multilingual children is differentiating between effects of language experience and genuine impairment when clinicians often lack suitable norm-referenced assessments. In this tutorial we demonstrate, via a case study, that it is feasible to identify DLD in a multilingual child using the CATALISE diagnostic criteria, Language Impairment Testing in Multilingual Settings (LITMUS) assessment tools, and telepractice. METHOD: This tutorial features a case study of one 6-year-old Urdu-Cantonese multilingual ethnic minority child, and seven age- and grade-matched multilinguals. They were tested via Zoom using Urdu versions of the Multilingual Assessment Instrument for Narratives (LITMUS-MAIN), the Crosslinguistic Lexical Task (LITMUS-CLT), the Crosslinguistic Nonword Repetition Test (LITMUS-CL-NWR), and the Sentence Repetition Task (LITMUS-SRep). RESULT: The child scored significantly lower in the LITMUS tests compared to her peers in her best/first language of Urdu. Together with the presence of negative functional impact and poor prognostic features, and absence of associated biomedical conditions, the findings suggest this participant could be identified as having DLD using the CATALISE diagnostic criteria. CONCLUSION: The result demonstrates the promise of this approach to collect reference data and identify DLD in multilingual children. The online LITMUS battery has the potential to support identification of multilingual DLD in any target language.
In today’s interconnected digital age, English language use is increasingly shaped by complex social forces. This study explores the influence of social tension, racial discourse, mass media, and popular entertainment on contemporary English practices. Grounded in sociolinguistic theory, critical discourse analysis, and media studies, the research examines how language functions as a tool of identity, resistance, and ideological negotiation. A structured questionnaire was distributed to English language learners, and the quantitative data were analyzed using SPSS. The results reveal that media platforms particularly music, television, and online content serve as influential sources of informal language acquisition, fostering lexical creativity and stylistic variation. Social media and digital entertainment were found to accelerate exposure to evolving linguistic norms. Additionally, social tensions and racial discourse significantly shape the emotional tone and political framing of everyday communication. These forces not only affect how language is used but also how it is perceived in relation to power, inclusion, and cultural identity. The study concludes by highlighting the need for culturally responsive language education that integrates an awareness of the socio-political contexts in which language is used, encouraging learners to critically engage with the linguistic influences that shape their communicative practices.
Introduction. Writing foreign-language creative writing assignments is one of the goals of foreign language teaching in higher education. Modern AI tools (ChatGPT 4.0) are able to provide learners with evaluative feedback and recommendations for essay revision. However, the feedback quality from ChatGPT 4 and other AI tools is a subject of discussion in the teaching community. The aim of this paper is to compare the feedback quality provided by ChatGPT 4.0 and teachers in evaluating students' essays. Materials and methods. A bank of English essays (N=350) written by linguistics students (A2-B1 level) was used as the material. The participants of the research were 12 teachers of English at Derzhavin Tambov State University (Russian Federation). For every essay, one teacher and ChatGPT gave evaluative feedback on the following criteria: 1) content of the essay; 2) organisation and structure of the essay; 3) supporting ideas and arguments; 4) language of the essay (lexical aspect of speech, grammatical aspect of speech, syntax); and 5) originality of the idea. The recommendations received from the teacher and ChatGPT 4.0 were evaluated on the basis of norm-referenced testing. The data analysis was carried out using the Student’s t-test. Research results. It was found that ChatGPT matched the teacher in terms of the quality of evaluative feedback for the following criteria: ‘content of the paper’ (t=0.24; p>0.05), ‘organisation and structure’ (t=1; p>0.05), and ‘supporting ideas and arguments’ (t=1.43; p>0.05). Moreover, ChatGPT outperformed the teacher (not a native speaker) for the criteria ‘language of the essay’ (t=1.67; p≤0.05) and ‘originality of the essay’ (t=1.78; p≤0.05), which is explained by the fact that the GPT language model was developed based on large English textual data. This allowed the AI tool to be more accurate in assessing specifically the linguistic correctness of a written expression. Conclusion. The novelty of the study is in the confirmation of the ability of the AI tool ChatGPT 4.0 to provide qualitative feedback in assessing creative writing at the teacher's level or even better. The results of the study support more intensive implementation of ChatGPT 4.0 in the process of teaching foreign language and assessing the development level of students' writing skills.
This article analyzes lexicosemantic processes in contemporary Ukrainian scientific texts against the backdrop of general Slavic trends in literary norms. It traces the activation of rarely used vocabulary, the reactivation of obsolete terms, determinologization, the broadening of the meaning of specialized terms, and the functioning of foreign language lexical units. The article highlights word-formation tendencies in scientific style, taking into account the internal laws and potential capabilities of the Ukrainian language. In terms of stylistic norms, the study has identified excessive expressivity in texts due to the use of linguistic elements that are atypical for scientific discourse, such as colloquialisms, neologisms, and slang.
the article analyzes the translation of popular science literature from the perspective of stylistic uniqueness. Options for preserving the national-specific characteristics of a Finnish-language popular science text on the history of navigation in its Russian-language version are presented. Objective: to identify methods and techniques that ensure the preservation of the characteristic features of the popular science sub-style in translation from the Finnish to the Russian language, namely imitation of live dialogue with a reader, preservation of cultural and landscape realities, and stylistic features of an original text (repetition, introductory constructions, modal and evaluative words). Research materials: the translated text of the Finnish-language popular science book on navigation by D. Johnson and J. Nurminen “The History of Seafaring: Navigating the World’s Oceans”. Results and novelty of the research: in the Russian-language text, the Finnish national color is maintained through the use of toponyms, anthroponyms, and ergonyms. The translator flexibly employed two strategies – domestication in transformation of the syntactic structures of the original, its morphological forms, stylistic features, and foreignization in conveying linguistic and cultural realities and accompanying them with explication. The measured rhythm of Finnish sentences in the Russian language is conveyed through lexical repetition with syntactic expansion, various types of introductory constructions. A feature of the book’s style is the imitation of dialogue with a reader. In most cases, the stylistic norms of the Russian language allow the reproduction of the live contact between the authors and a reader, which is conveyed in the translation text through appropriate modal means. To convey emotional and evaluative content, grammatical and lexical means of expressing modality in the Russian language are used. The “dictum content” in the translated text is expanded, which is due to the intended purpose of popular science literature to present scientific information in an accessible form and popularize knowledge among a wide range of readers. The scientific novelty of the work consists in the fact that it is devoted to a little-explored theme related to the transfer of lexical and stylistic features of popular scientific literature on the history of navigation into the Russian language. For the first time, the work implements an integrated approach to the study of the stylistics of the Finnish-language text and examines the possibilities of translation into the Russian epithets expressing an assessment or judgment about the object of the authors of the book, amplifying particles, in preserving the modality of the original text by using adverbs with the meaning of confidence, possibility and assumption. In the translation into the Russian, the dictum content of the Finnish text undergoes some transformation. Morphological changes are caused by the requirement of stylistic norms of the Russian language.
Language acts as a primary vehicle for transmitting cultural norms, values, and expectations, as well as thought paradigms, from one generation to another. This is despite language choices in Ekegusii proverbs being loaded with culturally stereotypical language choices about gender. This study investigated how power discourses are employed in Ekegusii proverbs. The study applied Critical Discourse Analysis (CDA) by Fairclough (1989, 1993 and 2001) and Van Dijk (2001). This research adopted a descriptive qualitative research design. The target population was eighty (80) ekegusii proverbs. Purposive sampling procedures were used to select eighty power-related proverbs. The study adopted the three-dimensional discourse framework as a method of data analysis. The findings of the study revealed that power relations are embedded in Ekegusii proverbs. The lexicalization and level of meaning of proverbs showed that these proverbs obtain their sexist and obscene connotations, which are construed as being laden with an impertinent reference that derogates womanhood. The study further revealed that linguistic features such as metaphors, negative syntactic structures, and vocabulary are used to enact power between genders. The study recommends that gendered proverbs be consciously improved to portray gender neutrality, equality and contemporariness.
Abstract Critical comments have shown to figure prominently in determining the fate of manuscripts submitted to reputable journals. While various studies have explored different facets of this evaluative genre, there has been limited examination in the context of second language and disciplinary writing. Using a discourse analytic approach, this study analyzed a corpus of 160 reviewers’ reports on submissions by Iranian nonnative writers in applied linguistics (AL) and engineering. The aim was to compare how reviewers employ different categories of critical comments to prompt writers to revise their submissions. The findings revealed that reviewers, regardless of discipline, more frequently commented on language-use issues than content-related issues. Among language-use comments, issues pertaining to lexical and syntactical usage of English were more prominent than concerns about discourse and rhetoric. The analysis also indicated consistent patterns in the reviewers’ reports regarding discourse organization and the balance between positive and negative feedback. These findings are discussed in terms of their practical implications for novice and nonnative researchers in the examined fields, offering insights into the rhetorical and disciplinary norms governing peer reviews and the linguistic choices made by reviewers to guide authors throughout the review process. Increased awareness of these issues can facilitate more effective responses to reviewers’ feedback.
While browsing my Facebook feed on an early summer day in May 2017, a post with the trigger warning “inconsistent use of pronouns” grabbed my attention. The post, shared within a private Facebook group for (foreign) LGBTQIA+ individuals and allies living in Japan, featured an article recently published in The New York Times. Titled “Japanese Transgender Politician is Showing ‘I Exist Here’,” the article focuses on Hosoda Tomoya, a Japanese trans man who recently won a seat in the local city council in a suburb just outside of Tokyo (Rich, 2017). Hosoda made history as the first trans man in the world to be voted to public office, and the near-full-page article delved into Hosoda's life history, his journey into politics, and the challenges that he faced as a trans person living in Japan. What the author and the subsequent commenters of the Facebook post found “baffling” about the article was the use of the pronoun “she” when referring to Hosoda's childhood years as a girl named Mika, whereas throughout the remainder of the article, “he” was used to refer to Hosoda. This inconsistency was deemed by some as “poor etiquette,” particularly from a reputable outlet like The New York Times. What the readers were not aware of, however, was that Hosoda himself had approved the use of the pronoun “she” in that specific section of the report. The reason he provided was that it is an undeniable fact that he had “publicly lived as a woman before chiryo” (transition, literally medical treatment) and therefore did not see anything wrong with using the feminine third-person pronoun (private communication). If Hosoda himself did not find the pronouns “inconsistent” or offensive, should the general readers take issue with them? In the middle of 2020, I received an email from a graduate student based in the United States who had recently read one of my articles. The student took issue with my use of the term “FTM,” pointing out that by using it to refer to my research informants, I am perpetuating the “linguistic violence” associated with the term. In that article, I drew on my fieldwork in what I term the Japanese FTM community in Tokyo to show how seemingly mundane social events, such as drinking parties that are organized by and for trans men, can function as a site for my informants to negotiate inclusion and belonging as trans without undermining their male public selves. Within this community, “FTM” (the English acronym for female-to-male transgender) is the preferred term of self-reference, both in written form and in speech (transliterated as efu-tii-emu in Japanese). Although I was aware of the debates surrounding this term in English-speaking contexts, where it is considered outdated and criticized for emphasizing a notion of change that contradicts the experiences of many trans individuals who have always identified as such, I chose to use it to refer to my informants because they have consistently used it to describe themselves and others within the community. The term “FTM,” originally borrowed from English, is used in the Japanese context to describe individuals whose karada no sei (literally the sex/gender of the body) is female but whose kokoro no sei (literally the sex/gender of their heart) is male. Alongside its counterpart “MTF” (male-to-female transgender, transliterated as emu-tii-efu), these terms have gained prominence in both government publications and writings by transgender individuals as appropriate labels to reference toransujendā (the Japanese transliteration of the English term transgender). Toransujendā, as a new category of personhood, emerged in Japan in the late 1990s following the introduction of the medical condition seidōitsuseishōgai (Gender Identity Disorder, henceforth GID) along with the official recognition of seibetsutekigō shujutsu (gender affirming surgery, literally sex reassignment surgery) as the appropriate “treatment” for GID. Despite the recent global trend toward demedicalization, transgender in Japan remain predominantly understood in medical terms, and the “wrong body” narrative continues to be invoked by many trans individuals seeking access to hormones, surgery, and legal gender recognition. In that article, I should have explicitly acknowledged the potential harm that the term, along with notions of medicalization associated with the term, may inflict on certain trans individuals. I should have also pointed out that I recognize that not all trans men in Japan identify with and use the term “FTM.” Perhaps I could have used the Japanese transliteration efu-tii-emu instead. Nevertheless, based on my analysis of Japanese trans autobiographies (such as Sugiyama, 2006) and from my fieldwork, I observed that the medical discourse has played a significant role in providing a new and legitimate way for understanding gender non-normativity in Japan—an aspect traditionally associated with the realm of sex and entertainment (McLelland, 2005). The emergence of terms like “FTM” has empowered many Japanese trans individuals, enabling them to move from an unspeakable, “unacceptable and abominable existence” (Sugiyama, 2006, pp. 65–66) to one that is comprehensible and sanctioned by the authority of medicine. The enactment of a law in 2004—the Exceptional Treatment Law for People with Gender Identity Disorder (Seidōitsuseishōgaisha no seibetsu no toriatsukai no tokurei ni kansuru hōan)—that allows trans individuals who have received a GID diagnosis and who have undergone gender reassignment to legally change their gender further legitimizes their existence.i Most, if not all, of my informants—who come from diverse backgrounds and are at various stages of transition—identify with and embrace the label “FTM.” Many even prominently feature it on their social media profiles. Although some individuals may not identify exclusively with the term “FTM,” given the term's cultural significance within the Japanese context, would it not also be an act of violence if I were to adopt a different term for this community, thereby erasing its history and denying my informants of the subjectivity and empowerment that “FTM” affords them? Both episodes reminded me of an incident at the 2009 Netherlands Transgender Film Festival recounted in Leung (2016). During the screening of the documentary Transvestites Also Cry (2007), which follows the lives of two Ecuadorian migrant sex workers in Paris, several audience members walked out of the theater in protest. They were apparently upset by the filmmaker's use of the pronoun “he” to refer to the protagonist in the film, who identifies as female. They were also offended by the term “transvestite,” which the filmmaker used as the translation for the Spanish term travesti, a term that the subjects in the film used to refer to themselves and others in their community. As Leung (2016) rightly observed, the filmmaker could have avoided controversy by titling the film more “correctly,” albeit somewhat awkwardly, as Transgender People Also Cry. However, even with a more accurate title, the subjects’ use of pronouns was never consistent, and they also employed terms like “homosexual” and “tercer sexo” (third sex) alongside “travesti” to describe themselves and their friends. Other visual cues in the film further underscored the “noncoherence between categories, identities, and experiences” (ibid., p. 435). What we can glean from these examples is that although “correct” terminologies hold significance, they cannot fully capture the complexity of all trans lives and experiences. What may be considered acceptable or preferred terminology in one context may lack relevance or appropriateness in another. Yet, the dominance of Anglo-centric perspectives in public and academic discourses surrounding queer and trans issues has led to the widespread assumptions about the universal applicability of English terminologies and identity categories. As a result, local ways of understanding and articulating diverse gender identities and embodiment—which emerged out of historical, social, cultural, and political contexts that may differ significantly from those of the Anglo-West—may become overshadowed or replaced entirely by the ostensibly more “correct” terminology and the associated notions of trans identity that they convey (Leung, 2016). I certainly do not doubt the importance of using trans-affirming language. As Zimman (2017) observed, language is one significant site through which trans identities are negotiated, validated, and undermined. The advocacy for transgender language reform, a cornerstone of trans activism, has catalyzed a critical reevaluation of linguistic practices in many Anglo-Western societies in recent years. From the adoption of gender-neutral pronouns (such as singular they/them/theirs) to the development of new gender identity terms (such as the word “non-binary”), the push for trans-affirming and gender inclusive language has challenged the normalization of transphobia and cissexism in everyday language use, prompting better recognition and affirmation of trans people's self-identities (ibid). However, as Cameron (2012 [1995]) reminded us, “language is a highly variable and radically context-dependent phenomenon which may have effects on perception, but only in conjunction with other factors” (p. 142). Although language can perpetuate certain beliefs and assumptions, it alone does not create them. Terminologies and perspectives are inter-related, yet perspectives are not universal. Therefore, it is crucial to consider language within its broader sociocultural context. Returning to Hosoda's case, many English-speaking readers today might similarly find the use of a female third-person pronoun for someone identifying as male inappropriate. Although Japanese language is generally perceived to be gendered—an ideology often reinforced in school textbooks, media, and daily conversations—pronouns, in normative usage, do not solely index gender identity. As Morita (2003) highlighted, “Japanese personal pronouns always index a specific social relationship […] Japanese speakers must choose certain address and reference terms to locate themselves as well as their interlocutors in the entire speech community to which both of them belong, giving an acknowledged role in society to each other” (p. 371). As such, Japanese speakers may use different pronouns depending on the social context, taking into consideration factors like gender, age, and status in relation to their interlocutors, all while adhering to lexical items appropriate for their gender. Gender, within the ideology of gendered language, is treated as a singular, unified concept where various aspects of gender, such as gender identity, assigned gender, legal gender, and gender presentation, are conflated into one. As a result, for many Japanese-speaking trans individuals navigating these gendered language norms, the choice of pronouns may not always be straightforward, leading to situations where they switch between “masculine” and “feminine pronouns” depending on the context. In the community that I studied, many trans men use the vulgar first-person “masculine pronoun” ore when speaking to their peers or younger members in the community. Those from working class backgrounds also tend to favor ore over boku (a first-person “masculine pronoun” used by men in informal settings), which is more commonly used among middle-class trans men or when addressing older or senior members in the community. The choice of masculine pronouns by these trans men not only indexes male gender identity or male social gender but also conveys additional nuances. For instance, the use of ore indexes attributes such as coolness and assertiveness (see also Miyazaki, 2023), whereas boku carries connotations of youthfulness, approachability, and polite informality. However, in professional settings outside of the community and on social media platforms, some of these trans men, who use ore and boku within the community but who have not come out or undergone transition, opt for “feminine pronouns” like watashi (a first-person pronoun usually used by women, but also used by men in formal settings) and kanojo (she). This choice, akin to Hosoda's situation, aligns with gendered speech conventions in Japan, where individuals typically (or are expected to) choose “feminine pronouns” if their assigned/social/legal gender is female. If criticism is warranted here, it should not be directed at Hosoda or The New York Times for their use of female pronouns on someone who identifies as male, but rather at the gendered language ideology that perpetuates concepts of “men's speech” and “women's speech” in Japan. Trans language activism (TLA) has indeed spread far and wide, reshaping ways of thinking and speaking about trans beyond the Anglophone sphere. In line with developments in the West, some trans activists in Japan today are also advocating for the use of “toransu man” (trans man) and “toransu ūman” (trans woman) as the more accurate and modern equivalents of “FTM” and “MTF.” However, as Zimman pointed out in the discussion article, the advocacy around trans-affirming language has largely been influenced by the perspectives of trans individuals and allies who hold privilege along other axes of identity. These individuals often tend to be white, highly educated, employed in elite academic institutions, able-bodied, and native English speakers. Using terminologies and linguistic practices deemed “correct” by a small group of trans activists and language reformers in a blanket manner, without consideration of the underlying power dynamics, can be problematic in several ways. Not only does it hinder TLA's goal of achieving sociolinguistic justice, but it can also perpetuate other forms of marginalization, such as the devaluation of Black language, as highlighted by Zimman. Furthermore, it can (re)produce a “hierarchy of experiences and subjectivities” (Leung, 2016), which privileges Euro-American understandings of gender non-normativity as modern and progressive while dismissing other (local) ways of imagining and expressing gender non-normativity as traditional and outdated (Leung, 2016; Grewal & Kaplan, 2001). To effectively achieve sociolinguistic justice, we need to provincialize Western perspectives on trans and its associated vocabularies (Chiang et al., 2018). Trans activists, individuals, and allies must move beyond Eurocentric thinking and recognize that the “correct” terminologies and categories originating from the Western trans movement may not universally apply to trans and gender non-conforming individuals in other cultures. Real progress toward sociolinguistic justice begins when we move beyond simply policing language use or correcting instances of what some may perceive as misgendering without considering the broader context.
Relevance. The need to investigate the specifics of Chinese translator training within the framework of the global programme �One Belt, One Road� is primarily conditioned by the constant changes in various aspects, particularly in structural, semantic, and artistic aspects. Purpose. The purpose of this study was to investigate the global initiative �One Belt, One Road� in the context of training professional human resources to perform Chinese language translations. Methodology. This study analysed materials available on the websites of several higher education institutions. The analysis focused on specific parameters such as the distinct features of training students in Chinese studies, the education of translators working with Chinese, and the principles, methods, and techniques used in teaching the Chinese language. Results. Analyses of translator training have identified key skills for students: parallel language and culture study, adherence to translation norms like business etiquette, and mastery of technical translation tools. In Kyrgyz universities, methods include verbal (lectures, discussions), visual (audio and video materials), and practical (creative works, exhibitions, quizzes, conferences). For Chinese translators under the �One Belt, One Road� initiative, training spans multiple linguistic levels: phonetic (phrases, accents), grammatical (categories, meanings), lexical (vocabulary related to the initiative and various fields), syntactic (word order, links), and stylistic (business etiquette, unique vocabulary). Conclusions. The study found that the �One Belt, One Road� initiative's language policy focuses on planning Chinese language status, foreign languages, language structure, educational language use, and language services. The key challenges are the shortage of skilled professionals, intergovernmental cooperation, infrastructure standards, financial integration, and cultural exchange. In the future, this study can be used to develop technical improvements in Kyrgyz-Chinese translation, to improve the training of Chinese specialists within the framework of the global initiative �One Belt, One Road�. Keywords: lingua franca; linguistic levels; linguistic policy; linguistic parameters; terminological units
Although previous work has contributed to our knowledge of bilingual compound verbs (BCVs) in different code-switching varieties, there is scant research on the semantic nature of these innovative constructions. To fill this gap, the present study examines semantic aspects of BCVs in Northern Belize and the Yucatan Peninsula in Mexico, two sociohistorically connected communities where Spanish hacer ‘do’ BCVs have been attested. Drawing on two datasets, we analyzed the semantic domains that are most open to other-language lexical verbs as well as the potential use of these structures as identity markers. The analysis of 1,140 BCVs (903 in Northern Belize and 237 in Yucatan) revealed that whereas ‘education’ particularly favored English lexical verbs in Northern Belize, ‘nourishment’ was the semantic sub-category most open to Yucatec Maya lexical verbs in the Yucatan Peninsula. Notably, only hacer BCVs from Yucatan evince the incorporation of cultural elements and linguistic practices such as albur ‘word play’ to index a Yucatec Maya ethnolinguistic identity. Our findings highlight the importance that the nature of bilingualism and community linguistic norms have on the semantic use of BCVs.
Abstract The paper presents ParlaMint-It, a new treebank of Italian parliamentary debates, linguistically annotated based on the Universal Dependencies (UD) framework. The resource comprises 20,460 tokens and represents a hybrid language variety that is underrepresented in the UD initiative. ParlaMint-It results from a manual revision process that relies on a semi-automatic methodology able to identify sentences that are most likely to contain inconsistencies and recurrent error patterns generated by the automatic annotation. Such a method made the revision process faster and more efficient than revising the entire treebank. In addition, it allowed the identification and correction of annotation errors resulting from linguistic constructions inconsistently represented in UD treebanks and from characteristics specific to parliamentary speeches. Hence, the treebank is deemed as an 18-karat resource, since, although not fully manually revised, it is a valuable resource for researchers working on Italian language processing tasks.
The rigid framework for interpreting any “code of rules” significantly limits the theoretical aspects of doing research on them and requires a special explanation in relation to orthography as a section of linguistics, especially when there are several national variants of the literary language. The relevance of the study is substantiated by the urgent need for a theoretical description of the corpus of the most significant concepts of spelling used in teaching Russian in Belarus and creating a strategic plan for introducing innovations in spelling; the need to record changes in definitions determined by the transition to convergent learning; the influence of a complex of linguistic and extralinguistic factors, and the development of optional arguments for the codification of the Belarusian national version of the Russian language. The aim of the study is to determine the parameters and specifics of the theory of orthography in educational literature for teaching Russian in the Republic of Belarus. The material of the study contained the definitions and language illustrations of the main concepts of the theory of orthography (spelling, spelling rule, orthogram, spelling principle, spelling error, spelling norm, etc.) in textbooks and manuals on the Russian language for higher education institutions of the Republic of Belarus published from 2000 to 2024. The methods of parameterization and comparison, logical-linguistic and lexical-semantic analysis were used. The authors revealed that both specialized educational publications devoted only to spelling and comprehensive publications where spelling is part of the educational material lack the theoretical and meta-linguistic apparatus of either a significant part or the entire necessary base of the theory of orthography. There are different approaches to the positioning of orthographic terminology that coexist in various educational publications. This is mainly an orientation towards outdated traditions, the absence of modern concepts (such as “orthographic activity”, “orthographic picture of the language”, etc.). The theory of orthography in educational literature on the Russian language for higher educational institutions of the Republic of Belarus is based, as a rule, on the modern rules of Russian spelling, but is characterized, on the one hand, by innovative specificity, and on the other hand, by their conservative positioning in writing practice. This indicates different approaches to the formation of the orthographic linguistic personality, orthographic linguistic and metalinguistic consciousness of Belarusians studying the Russian language. The main principles of modern theory of spelling development should take into account orthographic innovations and national and cultural specificity in teaching the Russian language in higher educational institutions of the Republic of Belarus.
This paper on lexiculture as a hidden referent in lexical teaching in Spanish as a foreign language (SFL) highlights the importance of lexical teaching in language acquisition, underlining its relationship with culture. Here we study how cultural understanding enriches vocabulary learning and communication in SFL. It is highlighted that the lexicon reflects cultural realities, values and beliefs, which requires recognizing these connections for an effective use of terminology. It also examines how culture influences the lexicon and vice versa, from culture-specific terms to idiomatic expressions rooted in the culture. Methodologically, the relationship between culture and lexicon is manifested in the expression of values, cultural identity, translation, cultural evolution, idiomatic expressions and social norms. Cultural terminology is essential to express significant aspects of a culture, such as food, festivities, kinship, idiomatic expressions of places, religion, traditional dress and nature. Therefore, the importance of adapting the lexicon to the cultural context under study without losing one's own identity is emphasized. For the treatment of lexiculture in the classroom, cultural immersion and exchange are proposed as tools for understanding and using the cultural lexicon effectively. This analysis also outlines some strategies for the teaching-learning of lexis and culture in order to understand this intrinsic relationship and enrich linguistic competence. It also points out a series of lines of research on the academic horizon to broaden the study of the relationship between culture and lexicon, considering it to be profound and essential in the study of SFL and, above all, highlighting how the lexicon is shaped by the culture in which it is immersed.
Old Permic, also known as Old Komi, is an extinct variety of Komi that was spoken in the late Middle Ages in the lower Vychegda river basin in Northeastern European Russia, in an area that currently is not Komi-speaking. This language variety is attested in fragmentary records from the 14th to 17th century written both in the Old Permic alphabet and in Cyrillic. These records are of significant importance for research on the history of the Komi language. Here we introduce our attempt towards a new Universal Dependencies treebank that will eventually contain the existing corpus of Old Permic in a structured and CoNLL-U annotated format. This will be the first time this material is being made openly available in digital format, and our contribution describes the current state of the art and remaining challenges.
We introduce Alma ( ﺍ ﺍ ﻠ ى ), an open-source and state-of-the-art lemmatizer, POS tagger, and root tagger for Arabic, boasting both high speed and accuracy. Alma relies on a dictionary of morphological solutions ordered by the frequency of these solutions. This dictionary was developed based on the Qabas lexicographic database. Unlike many Arabic lemmatizers that return a lemma after stripping diacritics, shadda, and hamza (i.e., ambiguous lemma), Alma retrieves unambiguous lemmas (we called true lemmatization). Our POS tagger uses a rich tagset of 40 POS tags. Additionally, our root tagger is the first fully-featured tagger since it uses Qabas, the largest Arabic lexicographic database. We evaluated Alma on the LDC Arabic Treebank (ATB) that contains 339,710 tokens and achieved an 88% F1 score. We also evaluated Alma on the Salma corpus (34k tokens) and obtained a 90% F1 score. Compared to Farasa, MADAMIRA, and Camelira lemmatizers and POS taggers, Alma outperformed all of them in both tasks, excelling in both speed and accuracy. Alma demonstrated superior processing speed, handling 339k tokens in 10.00. Alma is open-source and publicly available at ( https://sina.birzeit.edu/alma ).
Abstract The development of a benchmark for part-of-speech (PoS) tagging of spoken dialectal European Spanish is presented, which will serve as the foundation for a future treebank. The benchmark is constructed using transcriptions of the Corpus Oral y Sonoro del Español Rural (COSER;“Audible corpus of spoken rural Spanish”) and follows the Universal Dependencies project guidelines. We describe the methodology used to create a gold standard, which serves to evaluate different state-of-the-art PoS taggers (spaCy, Stanza NLP, and UDPipe), originally trained on written data and to fine-tune and evaluate a model for spoken Spanish. It is shown that the accuracy of these taggers drops from 0.98 $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 0.99 to 0.94 $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 0.95 when tested on spoken data. Of these three taggers, the spaCy’s trf (transformers) and Stanza NLP models performed the best. Finally, the spaCy trf model is fine-tuned using our gold standard, which resulted in an accuracy of 0.98 for coarse-grained tags (UPOS) and 0.97 for fine-grained tags (FEATS). Our benchmark will enable the development of more accurate PoS taggers for spoken Spanish and facilitate the construction of a treebank for European Spanish varieties.
Phrygian-KUL is a treebank of the ancient Phrygian language for Universal Dependencies (UD). Having originally only annotated the New Phrygian subcorpus, this dataset is continuously being updated to include the entire epigraphic corpus. For more information, please visit the relevant page at the UD project site or the repository on Github.
It has been suggested that humans use summary statistics such as the average of the emotion of individual faces when they rapidly judge group emotion. Previous studies have mainly used faces of actors posing basic emotions, and morphed versions of these faces, against a plain background. In the present study, photographs taken in real-world settings were used to investigate the influence of mean facial emotion, maximal facial emotion, and background context on judgments of group emotion, assessed using dimensional ratings of valence, arousal, and dominance. Background context explained a significant amount of unique variance in group ratings for each dimension. Mean emotion explained additional unique variance for valence ratings, whereas maximal emotion explained additional unique variance for arousal, with dominance showing more mixed results. Removing background context and disrupting the contextual and spatial relationship between faces by randomly replacing faces with ones from other images within the stimulus set increased reliance on mean emotion. However, under all conditions, the maximally arousing face continued to exert an influence on ratings of group arousal, in line with theoretical accounts arguing for a unique bottom-up effect of emotional arousal on attentional competition and postattentive perceptual processing. Together these findings suggest that individuals' reliance on average emotion when judging crowd scenes differs as a function of the dimension of affect. In addition, the presence of background context both directly impacts judgments of crowd emotion and modulates the relative influence of maximal versus mean emotion on these judgments. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Traditionally, emotions in dreams have been assessed using subjective ratings by human raters (e.g., external raters or dreamers themselves). These methods have extensive support and utility in dream science, yet they have certain innate limitations due to the subjective nature of the rating methodologies. Attempting to circumvent several of these limitations, we aimed to develop a novel method for objectively classifying and quantifying sequential (word-for-word) emotion within a dream report. We investigated whether sentiment analysis, a branch of natural language processing, could be used to generate continuous positive and negative valence ratings across a dream. In this pilot, proof-of-concept study, we used 14 dream reports collected upon awakening following overnight polysomnography. We also collected pre- and post-sleep affective data and personality metrics. Our objectives included demonstrating that (1) valence ratings derived from sentiment analysis (Valence Aware Dictionary for sEntiment Reasoning [VADER]) could be used to visualize (plot) positive and negative emotion fluctuations within a dream, (2) how the visual properties of emotion fluctuations within a dream (peaks and troughs, area under the curve) can be used to generate novel "emotion indicators" as proxies for emotion regulation throughout a dream, and (3) these emotion indicators correlate with sleep, affective, and personality variables known to be associated with dreaming and emotion regulation. We describe 6 novel, objective dream emotion indicators: Total number of Peaks, total number of Troughs, Positive, Negative, and Overall Emotion Intensity (composites from an "area under the curve" method using the trapezoid rule applied to the peaks and troughs), and the Emotion Gradient (a polynomial trendline fitted to the emotion fluctuations in the dream chart). The latter signifies the overall direction of sequential emotion changes within a dream. Results also showed that ⅚ emotion indicators correlated significantly with at least one existing sleep, affective, or personality variable known to be associated with dreaming and emotion regulation. We propose that the novel emotion indicators potentially serve as proxies for emotion regulation processes unfolding within a dream. These preliminary findings provide a methodological foundation for future studies to test and refine the method in larger and more diverse samples.
Emotion recognition from visual stimuli has emerged as a crucial area of research with wide applications in the field of Human-Computer Interaction (HCI) and mental health monitoring. Understanding and predicting emotional responses to visual stimuli from images is a critical task in affective computing. Our study uses deep learning and classical machine learning techniques to classify emotions based on color images. The OASIS image dataset was used; it contains multiple themes of images, including objects, scenes, persons, and animals, with their respective arousal and valence ratings. We applied k-means clustering to identify the number of data points in the maximum cluster within those ratings. We used a Convolutional Neural Network (CNN) regressor for feature extraction of images with their ratings and separately evaluated the error metrics of both the CNN and Random Forest regressor. The results imply that the CNN regression model performs better when predicting emotional dimensions than the Random Forest regression model. This model achieves lower MAE, MSE, and RMSE across the metrics. It shows a more precise and reliable performance in capturing the complexity of emotional dimensions.
How are concepts related to fundamental human experiences organized within the human mind? Our insights are drawn from a semantic network created using the Cross-Linguistic Database of Polysemous Basic Vocabulary, which focuses on a broad range of senses extracted from dictionary entries. The database covers 60 basic vocabularies in 61 languages, providing 11,841 senses from 3736 entries, revealing cross-linguistic semantic connections through automatically generated weighted semantic maps. The network comprises 2941 nodes connected by 3573 edges. The nodes representing body parts, motions, and features closely related to human experience occupy wide fields or serve as crucial bridges across semantic domains in the network. The polysemous network of basic vocabularies across languages represents a shared cognitive network of fundamental human experiences, as these semantic connections should be conceived as generally independent of any specific language and are driven by universal characteristics of the real world as perceived by the human mind. The database holds the potential to contribute to research aimed at unraveling the nature of cognitive proximity.
The paper explores the grammatical transformations in the paradigm of nouns from the cycle Za Serafimite (On the Seraphim) by St. John Chrysostom. The transformations detected have been compared to manuscripts from the 12th – 14th centuries: the Yagichev Zlatoust, Tarnovo recension of the Verse Prolog, the Synaxaria for the Triodion by Zacchaeus the Philosopher, the Life of St. Antony, the Chronicle of Manasses, Boril’s Synodikon, the Song of Songs from the Rila Monastery, Patriarch Photios’s Letter to Knyaz Boris, Philotheus Kokkinos’s translation of the Panegyric to All Saints. The paper also shows the chronology and frequency of the changes that took place. It explores the grammatical relations and the continuity between a pre-Euthymian translation and the Tarnovo Literary School, and makes a synchronic inquiry into a word class, which indicates the historical development of the standard Bulgarian language and the tendency towards analyticism.
Natural Language Processing (NLP) has transformed human-machine communication in the digital age, enhancing productivity and unlocking a wealth of possibilities. The effectiveness of NLP hinges on the availability of robust digital resources, such as extensive lexical databases and real-world language corpora. These resources are crucial for various NLP applications, including machine translation, text mining, and speech recognition.NLP's advancements hold immense promise to bridge communication gaps across cultures, provide deeper linguistic insights, and boost productivity across sectors, impacting education, industry, and economic development. However, challenges such as ethical concerns, the necessity for high-quality data, and potential biases in digital language resources must be addressed.This paper presents a vision for the digital resource industry as the cornerstone of NLP, focusing on quantitative transformations that tackle NLP challenges and facilitate big data management. Embracing these transformations, along with a robust digital resource industry, can significantly enhance human-machine interactions and drive future innovations.
Purpose We explored whether (1) an informational intervention improves ratings of individuals on the autism spectrum (IotAS) in a job interview by curbing salience bias and whether expert-based influence amplifies this effect (Study 1); (2) the effect of disclosure of autism on ratings depends on a candidate’s presentation as IotAS or neurotypical (Studies 1 and 2) and (3) social desirability bias affects ratings of and emotional responses to disclosers (Study 2). Design/methodology/approach In two studies, participants, randomly assigned to experimental conditions, watched a mock job interview of a candidate presenting as an IotAS or neurotypical and reported their perception of his job suitability and selection decision. Study 2 additionally measured participants’ traits associated with social desirability bias, self-reported emotions and involuntary emotions gauged via face-reading software. Findings In Study 1, the informational intervention improved ratings of the IotAS-presenting candidate; delivery by an expert made no difference. Disclosure increased ratings of both the IotAS-presenting and neurotypical-presenting candidates, especially the former, and information mattered more in the absence of disclosure. In Study 2, disclosure improved ratings of the IotAS-presenting candidate only; no evidence of social desirability bias emerged. Originality/value We explain that an informational intervention works by attenuating salience bias, focusing raters on IotAS' qualifications rather than on their unexpected behavior. We also show that disclosure is less helpful for IotAS who behave more neuronormatively and social desirability bias affects neither ratings of nor emotional responses to IotAS-presenting job candidates.
<p>Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of “felt” emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants—heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.</p>
Cite the source of the dataset as: Kolipakam, Vishnupriya, Michael Dunn, Fiona M. Jordan & Annemarie Verkerk. (2018). DravLex: A Dravidian lexical database. Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
The paper presents the tree editor TrEd and related tools that can be used to create, modify, browse, and search treebanks - large language corpora annotated with syntactic and/or semantic structure information. This might include not only phrase structure or dependencies, but also coreference, discourse analysis, and even inter-sentence relations. The project started in the year 2000, and it has been in continuous use since then at various institutions all over the world. Most of the tools are written in Perl, which makes them available to all major operating systems. For searching the treebanks, a query language was developed that describes sets of tree nodes and the relations between them. It also supports aggregation to produce quantitative outputs. There are two different implementations, one translates the queries into SQL statements, the other searches the data directly in the editor. Originally, TrEd supported the PML data format used for the Prague Dependency Treebank. To process data in a different format, one first needed to convert the data into the PML format (and possibly convert the modified data data back to the initial format). Later, a versatile extension system was added to TrEd which made it possible to support other data formats directly. We will show how this works on the example of Universal Dependencies. UD is a framework for grammar annotation across different human languages. The described extension allows TrEd (and some other tools) to open the files in the original UD format natively, building the internal representation on the fly, and also serialise them back after editing.
Abstract: The purpose of the current study was to develop a database of gaming photo stimuli to be used in future psychological research assessing behavioral, cognitive, and neural correlates related to gaming. Participants (ages 18-42, N = 549; 43.17% male) completed ratings on 119 gaming-related images across 5 different categories: valence, arousal, relevance, urge, and interest. A measure of gaming addiction was also included. Positive associations between gaming addiction scores and image ratings were predicted. Gamers rated images higher than non-gamers across multiple dimensions including valence (p =.0012), arousal (p <.0001), urge (p <.0001), and interest (p <.0001). Gaming addiction scores were positively associated with image ratings for valence, r =.399, arousal, r =.438, relevance, r =.215, urge, r =.550, and interest, r =.523, p <.0001. Finally, average image ratings for the overall sample ranged from 5.65 (SD = 2.04) to 3.63 (SD = 1.91) for relevance and interest, respectively. These findings suggest that databases of video gaming imagery, rated for valence, arousal, relevance, urge, and interest, could possibly be used in studies assessing cognitive processing of video gaming-related stimuli in individuals with problematic gaming behavior.
Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains. In this paper, we introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks, by leveraging multi-domain signals from the head (Meta Quest Pro VR headset), trunk (Equivital Vest), and peripheral (Empatica Embrace Plus) during affect elicitation via visual stimuli. Signals from 23 participants were recorded, alongside self-assessed valence and arousal ratings after each stimulus. LSTM layers extract features from each modality, while multi-scale attention captures fine-grained temporal dependencies, and SE blocks recalibrate feature importance prior to classification. We assess which domain's signals carry the most distinctive emotional information during VR experiences, identifying key biosignals contributing to emotion detection. The proposed architecture, validated in a user study, demonstrates superior performance in classifying valance and arousal level (high / low), showcasing the efficacy of multi-domain and multi-modal fusion with biosignals (e.g., TEMP, EDA) with IMU data (e.g., accelerometer) for emotion recognition in real-world applications.
A Dictionary of Old Norse Prose (ONP) is a lexicographic project that describes the vocabulary of the medieval language of Iceland and Norway. ONP has a complex history, transitioning from a traditional citation collection to a partial print publication and now existing as an online digital resource. The chapter is a case study that aims to identify and explore the essential components involved in establishing, developing, and advancing the dictionary over time. It also aims to examine the process of collecting lexicographic data, organizing them within a lexical database, and adapting the database to meet the project’s evolving needs. The study showcases how the database has expanded and transformed, becoming the fundamental component of the digital dictionary and playing a central role in the current online platform’s practical implementation and functionality. Ultimately, the study shows how a historical dictionary project has adapted to technological advancements and demonstrates multiple ways of using, enhancing and presenting various data that have been collected over a long period of time.
It has been posited that ingesting a pill constitutes a pivotal action that facilitates the effects of open-label placebos (OLPs: placebos honestly prescribed). In the present OLP experiment, the motor components of a placebo treatment were systematically varied. The participants (n = 183) were randomly allocated to one of four groups that all viewed aversive pictures. The ‘active OLP’ group took a placebo pill with specific instructions concerning the sequence of motor actions for the intake. The ‘usual OLP’ group swallowed the pill (without specific motor instructions), while the third group received an ‘imaginary OLP’ (no pill intake). The fourth group applied cognitive reappraisal (CR; active control group) to reduce emotional distress. The participants rated their affective state as well as the efficacy and plausibility of the treatment approach. Moreover, blood pressure and pulse were recorded as indicators of bodily arousal. The four groups did not differ in their valence ratings and physiological measures. The ‘imaginary OLP’ received higher ratings for both effectiveness and plausibility than the ‘usual OLP’. CR was rated as superior relative to all OLP conditions. In conclusion, reducing emotional distress with OLPs does not necessitate the consumption of a placebo pill. In terms of acceptability and ease of implementation, CR stands as a well-established alternative.
In this chapter, our main objective is to provide a succinct description of four leading models of discourse: Rhetorical Structure Theory, Segmented Discourse Representation Theory, the Penn Discourse Treebank project, and the Cognitive approach to Coherence Relations. We present the main goals of each model, and discuss their advantages and limitations. We also list their specificities compared to other models, and analyze the main differences between them. We focus more specifically on the aspects of these models that have to do with the description of discourse relations. For each model, we present the type of research to which it has been applied, and the data that have been produced in the form of annotated corpora. As we will see, all these models have been used to annotate large corpora with discourse relations. An important issue is therefore to establish mappings between the relations annotated in each of them, in order to compare data from one corpus to the others. At the end the chapter, we discuss various options for comparing annotations across models.
Depression, a pervasive mental health issue, highlights the critical need for early detection and effective intervention. A database (InStant-EMDB) is developed to analyze variations in the spoken responses of individuals with depression compared to healthy counterparts. Spoken responses are ob-tained from English and Malayalam bilingual speakers who respond spontaneously to a set of 15 emotionally evocative words. These words are sourced from the Affective Norms for English Words (ANEW) dataset, which includes valence and arousal ratings for each word. The speech in both English and Malayalam is manually transcribed to accurately reflect the spoken content. The dataset also contains self-reported af-fective ratings and data from a mental health survey (PHQ-9) collected from the participants to determine their mental state, which we considered the self-reported depression labels. A preliminary analysis is conducted on the collected speech using current state-of-the-art deep learning models such as Con-volutional Neural Networks (CNN), Long Short-Term Mem-ory networks (LSTM) and Bi-directional Long Short-Term Memory networks (Bi-LSTM). Although distinct linguistic patterns exhibited by individuals struggling with depression are successfully identified by all three models in both Malay-alam and English spoken responses, the highest accuracy is achieved by the LSTM models. Our findings and dataset em-phasize the potential of linguistic patterns as valuable cues for the early identification and intervention of depression and could contribute to enhancing accessibility for diverse populations.
To support language documentation, linguistic research, and acquisition of Sign Language of the Netherlands (NGT), we are expanding the NGT dataset in the lexical database Global Signbank. Our most prioritized goal is to add ca. 11,000 glosses (entries). We further aim at adding ca. 3,000 example sentences and to provide linguistic information with as many glosses as possible. As for linguistic information, Signbank allows for extensive phonological descriptions of signs, and the addition of multiple senses per sign, which we would like to connect to synsets in the Multilingual Sign Language Wordnet. Additionally, we are recording extra video data: we make multiple videos of the same sign, taken from different angles, and videos with non-manual expressions. Furthermore, we are collecting motion capture data, for improved (automatic) sign language recognition and production in the future. In this paper, we describe how we proceed, the decisions that have been made so far, and future uses of the dataset.
Abstract Reading emotional states of the interacting partner is fundamental for social communication. This ability of inferring others’ emotions is specialised for within-species communication, but is known to extend to cross-species interactions. Previous studies have suggested both morphological similarity and familiarity with the expressing species play a role in the success in cross-species emotion communication. To investigate the relative contribution of these factors in cross-species emotion perception of closely related species, humans and bonobos, we asked human participants with varying degrees of experience with bonobos to assign emotion labels to images of bonobo emotional expressions, and rate them on valence and intensity. Moreover, we investigated how the channel (face vs. body) and emotional valence (negative vs. positive) of bonobo expressions modulate the perception. The results show that experts agreed more on the labels assigned to positive and neutral faces and bodies than novices or intermediates, while negative bodies were perceived similarly by all three groups. Interestingly, novices showed a higher agreement score than experts and intermediates to label negative facial expressions. The effect of expert superiority for positive and neutral images was attenuated in valence ratings, and the ratings on negative faces remained difficult even for experts. Similar to the results of the emotional labels, novices agreed specifically well on the interpretation of the negative faces. For intensity ratings, expert superiority remained the same for facial expressions with negative facial expressions yielding the highest agreement scores in general. Our results indicate a mixed effect of similarity and familiarity: while novices predominantly use anthropomorphic strategies, experts drew upon their extensive knowledge to evaluate the emotional states from bonobo images. Bodily expressions showed similar effects of expert superiority, though not as strongly as facial expressions. Overall, experience plays a predominant role in cross-species emotion recognition.
• Unspeeded response time (URT) is negatively correlated with absolute valence and arousal. • URT is larger for food images with ambiguous valence. • URT is shorter for food images eliciting more arousing or intense affective responses. • The URT may be an implicit evaluation measure that complements self-report measures. Affective responses are often adopted as proxy measures of potential food choices. To reliably assess affective responses there is a need for implicit measures that are less prone to cognitive biases, context, lack of introspective capacity, social desirability, and intercultural differences than the explicit self-report measures that are commonly used. In this study, we investigated the relation between unspeeded response time (URT) and the affective appraisal (in terms of valence and arousal) for food images. We find that URT is negatively correlated with both absolute valence and arousal: URT is larger for food images that are rated near-neutral (ambiguous) on valence and low on arousal than for images eliciting more extreme positive and negative affective ratings. Participants need more time for the affective evaluation of food images with lower emotional clarity than those with clear-cut emotional quality. Hence, the URT may serve as a continuous and easily observable implicit evaluation measure that complements self-report measures.
Abstract Visual sexual stimuli (VSS) are often used to induce affective responses in experimental research, but can also be useful in the assessment and treatment of sexual disorders (e.g., sexual arousal dysfunctions, paraphilic disorders, compulsive sexual behaviors). This systematic literature review of standardized sets containing VSS was conducted by searching electronic databases (PsycINFO, PubMed, Scopus, Web of Science) from January 1999 to December 2022 for specific keywords [("picture set" OR "picture database" OR "video set" OR "video database" OR "visual set" OR "visual database") AND ("erotic stimuli" OR "sexual stimuli" OR "explicit erotic stimuli" OR "explicit sexual stimuli")]. Selected sets were narratively summarized according to VSS (modality, duration, explicitness, shown sexes, sexual practices, physical properties, emotion models, affective ratings) and participants’ characteristics (gender, sexual orientation and sexual preferences, cultural and ethnic diversity). Among the 20 sets included, researchers can select from ~ 1,390 VSS (85.6% images, 14.4% videos). Most sets contain VSS of opposite- and some of same-sex couples, but rarely display diverse sexual practices. Although sexual orientation and preferences strongly influence the evaluation of VSS, little consideration of both factors has been given. There was little representation of historically underrepresented cultural and ethnic groups. Therefore, our review suggests limitations and room for improvement related to the representation of gender, sexual orientation, sexual preferences, and especially cultural and ethnic diversity. Perceived shortcomings in experimental research using VSS are highlighted, and recommendations are discussed for representative stimuli for conducting and evaluating sexual affective responses in laboratory and clinical contexts while increasing the replicability of such findings.
The present research focuses on analyzing the mechanisms of politeness and the role of adjectives in interpersonal relationships, considering them as important tools for expressing attitudes, emotions, and cultural values. Based on the research results, it has been established that politeness strategies and the use of adjectives vary significantly depending on cultural contexts, underscoring the need for a deeper understanding of linguistic norms and variations. Special attention has been paid to the role of adjectives, which serve not only as descriptive elements but also as means of expressing evaluations and emotions, thus, playing a significant role in intercultural interaction. The research conclusions underscore the importance of integrating intercultural understanding into the process of linguistic interaction. It has been revealed that successful intercultural communication requires not only language proficiency but also a deep understanding of cultural differences in politeness strategies. The research also points to the need for further exploration in this field, in particular, in developing practical recommendations for enhancing intercultural communication. In this context, knowledge and application of relevant linguistic strategies can contribute to better understanding and respect among representatives of different cultures.
Natural Language Toolkit (NLTK) is a comprehensive Python library designed to facilitate the exploration, analysis, and processing of human language data. With its extensive collection of tools, NLTK provides researchers, developers, and educators with a powerful platform for tasks ranging from basic text processing to advanced natural language understanding and machine learning. The toolkit includes modules for tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, syntactic parsing, semantic analysis, and more. Furthermore, NLTK offers access to numerous linguistic resources such as corpora, lexicons, and treebanks, making it an invaluable resource for both learning and research in the field of natural language processing (NLP). NLTK serves as an indispensable tool for unlocking the complexities of human language
In everyday life, music is increasingly being listened to through headphones and mobile devices in public situations. While a large body of research has demonstrated that music may influence the emotional states of listeners and affect multimodal perceptions i.e. in films, less is known about the music’s impact on environments and the interpretation of social situations. We conducted an online experiment to investigate the influence of music on evaluations, considering individuals’ emotional states (emotion congruence) and group perception. Participants were randomly assigned to one of three experimental conditions (music with positive valence and high arousal, music with negative valence and low arousal, and no music) while viewing images of two different social group types that varied in perceived group characteristics (group members being familiar or unfamiliar with each other). Images were rated on four bipolar scales measuring affective quality and cognitive evaluation of social situations. Results show that individuals who listened to negative music provided lower valence ratings and also judgded social environments lower in terms of pleasantness and cheerfulness (affective) than individuals in the other experimental conditions. In contrast, ratings of crowdedness and familiarity (cognitive) did not differ between experimental conditions. The effect of music on affective evaluations was shaped by social group types, such that participants were more influenced by music when viewing intimacy groups (e.g., friends) than when viewing transitory groups (e.g., strangers). Overall, our results support the assumption of mood congruency for affective evaluations and emphasize the need to consider social information when studying the influence of music on the perception of environments.
Direct dependency parsing of the speech signal -- as opposed to parsing speech transcriptions -- has recently been proposed as a task (Pupier et al. 2022), as a way of incorporating prosodic information in the parsing system and bypassing the limitations of a pipeline approach that would consist of using first an Automatic Speech Recognition (ASR) system and then a syntactic parser. In this article, we report on a set of experiments aiming at assessing the performance of two parsing paradigms (graph-based parsing and sequence labeling based parsing) on speech parsing. We perform this evaluation on a large treebank of spoken French, featuring realistic spontaneous conversations. Our findings show that (i) the graph based approach obtain better results across the board (ii) parsing directly from speech outperforms a pipeline approach, despite having 30% fewer parameters.
Abstract Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.
Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.
This paper identifies a micro-cue correlating to verb second word order (V2) in two closely related Medieval Romance languages. As V2 is asymmetrically distributed in main rather than subordinate clauses, an asymmetry would be expected in phenomena assumed to relate to V2, such as subject inversion, null subject and enclisis. The loss of that asymmetry should therefore indicate the loss of the V2 word order rule. These assumptions are tested here by a quantitative analysis of a treebank of calibrated data covering the crucial period of change (from the 14th to the 16th century) for Medieval French and Venetian. The hard quantitative evidence provided demonstrates that the main versus embedded asymmetry is indeed a micro-cue of V2 structure, and of its loss in one of the two investigated languages.
Quantization is one of the efficient model compression methods, which represents the network with fixed-point or low-bit numbers. Existing quantization methods address the network quantization by treating it as a single-objective optimization that pursues high accuracy (performance optimization) while keeping the quantization constraint. However, owing to the non-differentiability of the quantization operation, it is challenging to integrate the quantization operation into the network training and achieve optimal parameters. In this paper, a novel multi-objective convex quantization for efficient model compression is proposed. Specifically, the network training is modeled as a multi-objective optimization to find the network with both high precision and low quantization error (actually, these two goals are somewhat contradictory and affect each other). To achieve effective multi-objective optimization, this paper designs a quantization error function that is differentiable and ensures the computation convexity in each period, so as to avoid the non-differentiable back-propagation of the quantization operation. Then, we perform a time-series self-distillation training scheme on the multi-objective optimization framework, which distills its past softened labels and combines the hard targets to guarantee controllable and stable performance convergence during training. At last and more importantly, a new dynamic Lagrangian coefficient adaption is designed to adjust the gradient magnitude of quantization loss and performance loss and balance the two losses during training processing. The proposed method is evaluated on well-known benchmarks: MNIST, CIFAR-10/100, ImageNet, Penn Treebank and Microsoft COCO, and experimental results show that the proposed method achieves outstanding performance compared to existing methods.
Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques.For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear.For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints.We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.1 For example, for each type (unique word form) in the GUM English Universal Dependencies Treebank (see https://universaldependencies.org/) there are 10.7 tokens.For the Latin PROIEL treebank there are only 6.5, and for the Greek Perseus treebank even less, viz.4.8 (note that they are all roughly similar in size: 212K, 205K and 202K tokens respectively).
Background: Despite the frequent comorbidity of affective and addictive disorders, the significance of affective dysregulation in problematic pornography use (PPU) is commonly disregarded. The objective of this study is to investigate whether individuals with PPU demonstrate increased sensitivity to negative emotional stimuli in comparison to healthy controls (HCs). Methods: Electrophysiological responses were captured via event-related potentials (ERPs) from 27 individuals with PPU and 29 HCs. They completed an oddball task involving the presentation of deviant stimuli in the form of highly negative (HN), moderately negative (MN), and neutral images, with a standard stimulus being a neutral kettle image. To evaluate participants' subjective feelings of valence and arousal, the Self-Assessment Manikin (SAM) was employed. Results: Regarding subjective evaluations, individuals with PPU indicated diminished valence ratings for HN images as opposed to HCs. Concerning electrophysiological assessments, those with PPU manifested elevated N2 amplitudes in response to both HN and MN images when contrasted against neutral images. Additionally, PPU participants displayed an intensified P3 response to HN images in contrast to MN images, a distinction not evident within the HCs. Discussion: These outcomes suggest that individuals with PPU exhibited heightened reactivity toward negative stimuli. This increased sensitivity to negative cues could potentially play a role in the propensity of PPU individuals to resort to pornography as a coping mechanism for managing stress regulation.
Conventional continuous emotion prediction systems are typically trained to predict the ‘average’ of affect ratings obtained from multiple human annotators. These systems, however, ignore the ambiguity inherent in the perceived emotions, which is not captured by the ‘average rating’. This paper presents a novel ambiguity-aware continuous emotion prediction system that predicts the time-varying emotion state as a series of beta distributions. Our recent work has shown beta distributions to be an effective parametric model of a collection of affect ratings. This work develops an appropriate cost function that enables neural networks to be trained to predict beta distributions. It also investigates the choice of parameterization of the beta distribution, the choice of activation functions of the output layer, and the tractability of gradient definitions in combination with the loss function. The proposed framework is implemented using a Bag-of-Audio-Words front-end and an LSTM-based back-end and evaluated on the RECOLA dataset. In addition to comparison with baseline systems that only predict the ‘average rating’, the effectiveness with which the predictions represent ambiguity in perceived emotions is also evaluated. Experimental results reveal that the proposed approach outperforms other ambiguity-aware systems, especially when predicting valence.