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A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization. The most common lexicalization strategies are the reuse and combination of existing words, but they have typically been studied separately in the areas of word meaning extension and word formation. Here, we offer an information-theoretic account of how both strategies are constrained by a fundamental tradeoff between competing communicative pressures: Word reuse tends to preserve the average length of word forms at the cost of less precision, while word combination tends to produce more informative words at the expense of greater word length. We test our proposal against a large dataset of reuse items and compounds that appeared in English, French, and Finnish over the past century. We find that these historically emerging items achieve higher levels of communicative efficiency than hypothetical ways of constructing the lexicon, and both literal reuse items and compounds tend to be more efficient than their nonliteral counterparts. These results suggest that reuse and combination are both consistent with a unified account of lexicalization grounded in the theory of efficient communication. [ABSTRACT FROM AUTHOR]

We propose an economic lexicon (EL) specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: (1) to have a wide coverage of terms used in documents discussing economic concepts, and (2) to provide a human‐annotated sentiment score in the range [−1, 1]. We illustrate the use of the EL in the context of a simple sentiment measure and consider several applications in economics. The comparison to other lexicons shows that the EL is superior due to its wider coverage of domain relevant terms and its more accurate categorization of the word sentiment. [ABSTRACT FROM AUTHOR]

Color terms show a remarkable variation in their possible lexicalization patterns across different languages. In the literature, the interest has been especially to describe the color lexicon of a certain language and to determine whether it may abide or not by Berlin and Kay’s universal evolutionary sequence, e.g., whether a certain color denomination may be considered as a basic color term, or whether a certain color category is lexicalized by more than one basic color term, by which criteria, etc. It has not been established, however, which are the most common lexical sources of basic color terms on a comparative basis, and which semantic changes are more common in this semantic field. On the basis of data drawn from 70 ancient and modern Indo-European languages, we aim at answering precisely these research questions concerning the origin and the development of basic color terms. We discuss the various lexical sources of the basic color terms for white, black, red, green, yellow, blue, brown, gray, orange, pink, and purple, and we show the most important semantic changes leading to these color meanings. We also discuss to which extent these terms are likely to be inherited or borrowed. All this aims at being a contribution to the study of diachronic semantics. [ABSTRACT FROM AUTHOR]

A comprehensive and high-quality lexicon plays a crucial role in traditional text classification approaches. It improves the utilization of linguistic knowledge. Although it is helpful for this task, the lexicon has received little attention in current neural network models. First, obtaining a high-quality lexicon is not easy. Second, an effective automated lexicon extraction method is lacking, and most lexicons are handcrafted, which is very inefficient for big data. Finally, there is no effective way to use a lexicon in a neural network. To address these limitations, we propose a pre-attention mechanism for text classification in this study, which can learn the attention values of various words based on their effects on classification tasks. Words with different attention values can form a domain lexicon. Experiments on three publicly available and authoritative benchmark text classification tasks show that our models obtain competitive results compared with state-of-the-art models. For the same dataset, when we use the pre-attention mechanism to obtain attention values, followed by different neural networks, words with high attention values have a high degree of coincidence, which proves the versatility and portability of the pre-attention mechanism. We can obtain stable lexicons using attention values, which is an inspiring method of information extraction. [ABSTRACT FROM AUTHOR]

This study, based on Jiang's (2000. Lexical representation and development in a second language. Applied Linguistics 21(1). 47–77) bilingual lexicon model, investigates the learnability of collocations among 34 Japanese EFL learners and examines the influence of their L1 on such learning. An acceptability judgment task assessed the knowledge of three different types of collocations: English-only collocations that cannot be directly translated into Japanese (e.g., flat rate); congruent collocations that can be translated into Japanese without changing their meaning (e.g., cold tea); and Japanized collocations that are infelicitous in English, but felicitous in Japanese (e.g., yellow voice). After the task, participants translated the collocations and rated the difficulty on a four-point Likert scale. The relationship between the accuracy of these collocations and the translation difficulty rating scores was analyzed using mixed-effects logistic regression modeling to assess L1 influence. The results showed that with increasing L2 proficiency, learners tend to regard congruent and English-only collocations as acceptable, but even highly proficient learners did not fully reject the Japanized collocations. This suggests that as L2 proficiency increased, participants learned to accept felicitous collocations but did not learn to reject Japanized infelicitous ones. In addition, the influence of L1 was evident in English-only and Japanized collocations and could not be avoided by those with increasing proficiency. [ABSTRACT FROM AUTHOR]

Antonymy is the lexical relation of opposition. The nature of the oppositeness may differ – e.g., contradictory ('true'–'false') or gradable ('tall'–'short') – and there may be variation as to the relationship in their formal encoding, whether the antonyms are expressed as distinct lexical forms (e.g., true vs. false) or if one form is derived from the other (e.g., true vs. untrue). We investigate the relationship between the two members of 37 antonym pairs across 55 spoken languages in order to see whether there are patterns in how antonymy is expressed and which of the two antonym members is more likely to be derived from the other. We find great variation in the extent to which languages use derivation (labeled "neg-constructed forms") as an antonym-formation strategy. However, when we do find a derived form, this tends to target the member of the pair that is either lower in valence (positive vs. negative) or magnitude (more vs. less), in line with our hypotheses. We also find that antonyms that belong to a core set of property concepts are more likely to encode antonyms as distinct lexical forms, whereas peripheral property concepts are relatively more likely to encode the antonyms with derived forms. [ABSTRACT FROM AUTHOR]

Sentiment is employed in various fields, such as collecting web-based opinions for the formulation of governmental policies, measuring employee and customer satisfaction levels in business organizations, and measuring the sentiment of the public in political and security matters. The field has recently faced new challenges since algorithms must operate with highly unstructured sentiment data from social media. In this study, the authors present a new stacking ensemble method that combines the lexicon-based approach with machine learning algorithms to improve the sentiment analysis of tweets. Due to the complexity of the text with very ill-defined syntactic and grammatical patterns, using lexicon-based techniques to extract sentiment from the content is proposed. On the same note, the contextual and nuanced aspects of sentiment are inferred through machine learning algorithms. A sophisticated bat algorithm that uses an Elman network as a meta-classifier is then employed to classify the extracted features accurately. Substantial evidence from three datasets that are readily available for public analysis re-affirms the improvements this innovative approach brings to sentiment classification. [ABSTRACT FROM AUTHOR]

While the integrity of academic work has always been vitally important, since the establishment of the International Center for Academic Integrity in 1992 increasing attention has been paid to the area. The term academic integrity now explicitly appears in policy and in job titles or offices tasked with either detection, training, or both. Equally, regulatory, quality and standards agencies, such as Quality and Qualifications Ireland (QQI), the Quality Assurance Agency for Higher Education (QAA) in the UK, and the Australian Tertiary Education Quality and Standards Agency (TEQSA) have mobilised around academic integrity with frameworks, lexicons, charters and networks that offer national guidance and promote shared action and understanding. However, despite efforts to interrogate and standardise the use of the term 'academic integrity' from those working and researching in the field, it is often used inconsistently and frequently conflated with its opposite – academic misconduct – in practice. Our aim, in this paper, is to acknowledge a lack of clarity around the term 'academic integrity' and to address this ambiguity by offering a taxonomy of academic integrity. We will arrive at the taxonomy of academic integrity through philosophical conceptual analysis and making use of pre-existing philosophical work in virtue (and vice) epistemology. The outcome of this analysis will be a taxonomy of academic integrity that represents the various concepts related to it in an organised way, classified into distinct groups, along with a graphic to aid understanding. [ABSTRACT FROM AUTHOR]

Jooshani dialect belongs to the Jooshan village, one of the villages of Golbaf town in Kerman city, Iran. The study tries to linguistically investigate and analyze the phonological processes of Jooshani. It also tries to present some grammatical and lexical features of this dialect. Here, Jooshani features are compared with those of Standard Persian (SP) and Kermani. The data have been gathered during nine years by one of the authors living in Jooshan and through close relations with the informants, most of whom were old, illiterate, and untraveled. The data are presented in three main parts: phonological system and phonological processes (metathesis, insertion, elision, lenition, spirantization, alternation, assimilation and devoicing), noun word-formation, and lexicon. The results obtained in each part showed that there are considerable differences between this language variety and the Standard Persian in all of the three above-mentioned parts. [ABSTRACT FROM AUTHOR]

This study introduced an app, called Smart UEnglish, for helping EFL conversation practices in authentic contexts. These conversation practices were categorized into 'designed talk' and 'free talk', based on the content of an English textbook and authentic ambient environment that includes such things as transportation, weather and scenic descriptions, respectively. The mechanisms in Smart UEnglish were designed in consideration of flexible, sustainable and adaptive conversations to improve English-speaking and lexical resources. Statistical analysis results show that the learning achievement of the experimental group is significantly higher than that of the control group. Moreover, both 'designed talk' and 'free talk' have significant positive correlations with learning achievement. Further analysis with stepwise multiple regression found that 'designed talk' plays the most important role and can significantly influence learning achievement. This is because 'designed talk' can motivate them to practice what they learned in class during their daily conversations and consolidate the understanding and applying of English vocabularies and sentence patterns through 'designed talk'. According to interview records, participants also perceived that using Smart UEnglish for conversation practice in authentic contexts can stimulate their interests and ideas to think and practice more useful English conversation. Hence, Smart UEnglish can effectively help English-speaking and conversation in authentic contexts anytime and anywhere. [ABSTRACT FROM AUTHOR]

The present paper aims to investigate the interplay of lexical and grammatical development in school-aged Greek-Albanian bilingual children by providing evidence both from majority, Greek, and heritage, Albanian. To this end, 47 8 to 10-year-old bilingual children were tested by means of expressive vocabulary tests in Greek and in Albanian, while their grammatical skills were evaluated by means of the LITMUS Sentence Repetition Task administered in both languages. Additionally, input-related measures in both languages, such as oral language practices, literacy, and educational practices were used to assess the bilingual experience and examine how it would correlate with lexical and grammatical skills. The analysis showed that within each language, lexical and grammatical skills were closely related both for the majority and the heritage language, while when examining across-language effects, grammatical skills in the heritage language were correlated with grammatical skills in the majority language. Concerning the role of input, we found that input in the heritage language supported skills both in the heritage and the majority language. The study's contribution is that by providing novel evidence from this specific language pair it shows that supporting the use of heritage language can boost language skills in both languages. [ABSTRACT FROM AUTHOR]

This paper examines links between perfect rhymes and text readability and decoding using a measure of English rhymes called the Perfect Rhymes Dictionary (PeRDict). PeRDict is based on the Carnegie Mellon University Pronouncing Dictionary (the CMUdict) and provides rhyme counts for ∼48,000 words in English and for the most frequent 1,000, 2,500, 5,000, and 10,000 rhymes within the dictionary as measured by the Corpus of American English (COCA). Two assessments of PeRDICT are presented. The first examines the strength of rhyme features to predict text readability in conjunction with word neighborhood density effects reported by the English Lexicon Project (ELP) and a word frequency measure. The second examines the strength of rhyme features to predict word decoding in conjunction with word neighborhood effects and a word frequency measure. In both assessments, the number of rhymes per word was predictive of text or word processing beyond features related to word neighborhood effects and word frequency. Word rhyme counts performed more strongly in predicting text processing versus word processing. [ABSTRACT FROM AUTHOR]

This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the relationship between gestures and vocabulary size in 177 Swedish‐learning 14‐month‐old children was examined. Gesture use, receptive, and expressive vocabulary were reported by caregivers with the Swedish version of the MacArthur Bates Communicative Developmental Inventory, words and gestures, SECDI‐1. Gesture types examined were referential gestures classified as either deictic gestures, conventional gestures, and object actions. A fine‐grained analysis of gestures and lexicon was performed. Results show that percentage of gestures used by children significantly predicts percentage of words in their receptive vocabulary. However, looking at gesture type, only use of object actions significantly predicts percentage of words in the receptive vocabulary whereas use of conventional gestures does not. Deictic gestures showed a ceiling effect and were therefore not further used for analysis. The relationship between gesture use and vocabulary size was not impacted by semantic category (food or clothes). Vocabulary in both semantic categories was statistically predicted by object actions in only one semantic category. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Despite the progress in gene editing platforms like CRISPR/Cas9 with the potential to transform the standard of care for haemophilia, the language used to explain and discuss gene editing is not aligned across the haemophilia community. Here, we present the objective and rationale for developing a clear, consistent, and globally aligned gene editing lexicon to address these communication gaps.Effectively communicating complex gene editing concepts requires a clear and consistent vocabulary. Through collaboration with a diversity of haemophilia stakeholders, our main goal is to develop an accurate, informative lexicon which avoids overpromising or highly technical terminology. Using an innovative process, representatives from several patient and scientific haemophilia organizations and select biotechnology companies will develop and refine language concepts to be tested with approximately seventy participants across the United States of America, United Kingdom, and Germany. Participants will include lived experience experts (LEEs) and haematologists. The process will be overseen by the Lexicon Steering Committee of global experts from leading scientific and patient organizations in the haemophilia and gene editing fields.Initial feedback provided a robust foundation and rationale for building clear, consistent language around gene editing. This lexicon development framework will allow for increased understanding across the haemophilia community, including the development of valid informed consent and shared decision‐making materials.Results provide important building blocks for stimuli development and highlight the need for a novel gene editing lexicon. In the next phase, language stimuli will be tested with LEEs and haematologists to better understand audience preferences and help shape the final lexicon. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the online discourse of Kazakhstan from the point of view of realization of structural-semantic and linguocultural parameters of online communication. In this paper, key parameters of structural semantics and linguoculture in Kazakhstani social networks are studied using lexico-semantic, structural and linguocultural analyses. The structural-semantic model of the lexicon of Kazakhstani social networks consists of two levels. The first level includes such lexical-semantic groups as emotional-evaluative and expressive lexicon, borrowed lexicon, linguoculturally marked lexicon, dialect lexicon, reduced and abnormal lexicon. Emotional-evaluative and expressive lexicon was represented by such groups as pejoratives (with negative evaluation) and melioratives (with positive evaluation). Linguoculturally marked lexis was represented by three main lexico-semantic fields: non-equivalent lexis, phraseological expressions and proverbs, and reduced and non-normative lexis was represented by colloquial and commonplace lexis. The statistical results showed that the quantitative correlation between linguocultural lexemes was distributed in the following way. Lexemes related to material culture were represented by 34%), lexemes related to professional activities were represented by 25%, lexemes related to food names were represented by 23%, and lexemes related to dance names occupied about 18% of the total. [ABSTRACT FROM AUTHOR]

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research. [ABSTRACT FROM AUTHOR]

Microblogs and social media sites have gained a central place and people use these platforms to express their opinions, sentiments, and thoughts about products, news, events, blogs, etc. Sentiment analysis is the process of exploring opinions and sentiments in user reviews and tweets. This area is still in its early developmental phase and requires imperative improvements on various issues. One of the main issues is multilingual tweets and reviews. Earlier sentiment analysis techniques only classified the text of a specific language, i.e., English, Turkish, etc. The accuracy of these techniques decreases in the presence of multilingual text. Existing methods are domain oriented. Using BERT and a lexicon, we propose a method for sorting out multilingual text and improving the polarity calculation of reviews. Experimental results reveal that our proposed technique achieved 90.14% accuracy and outperformed existing aspect-based sentiment analysis techniques. [ABSTRACT FROM AUTHOR]

In this article, I attend to the politics of world making in subaltern struggles against agrarian displacement by listening closely to Vietnamese villagers' refusal to be displaced. I argue that rendering these struggles intelligible through the prevalent framework of rights obscures the ontological violence and stakes involved. It is crucial to be vigilant of not only the limitations of rights' (in)capacity to address wrongs, but also the implication of the rights discourse in unequal power relations and global hegemony. To contemplate politics and ethics 'after rights' is to explore globally diverse languages and desires for just relations that have long existed before and alongside rights. It requires us to be mindful of the complex layers of subaltern speeches and actions beyond the dominant lexicon. Here I examine carefully the hermeneutic and ontological layers of peasants' claims of being wronged ('nông dân oan') in their battle against displacement in Việt Nam. I suggest that their articulations of violation, dialogue, and revolt open us up to conceptions of being human and being just alternative to those contained within rights discourse. [ABSTRACT FROM AUTHOR]

This study harnesses the linguistic diversity of Arabic dialects to create two expansive corpora from X (formerly Twitter). The Gulf Arabic Corpus (GAC-6) includes around 1.7 million tweets from six Gulf countries—Saudi Arabia, UAE, Qatar, Oman, Kuwait, and Bahrain—capturing a wide range of linguistic variations. The Saudi Dialect Corpus (SDC-5) comprises 790,000 tweets, offering in-depth insights into five major regional dialects of Saudi Arabia: Hijazi, Najdi, Southern, Northern, and Eastern, reflecting the complex linguistic landscape of the region. Both corpora are thoroughly annotated with dialect-specific seed words and geolocation data, achieving high levels of accuracy, as indicated by Cohen's Kappa scores of 0.78 for GAC-6 and 0.90 for SDC-5. The annotation process leverages AI-driven techniques, including machine learning algorithms for automated dialect recognition and feature extraction, to enhance the granularity and precision of the data. These resources significantly contribute to the field of Arabic dialectology and facilitate the development of AI algorithms for linguistic data analysis, enhancing AI system design and efficiency. The data provided by this research are crucial for advancing AI methodologies, supporting diverse applications in the realm of next-generation AI technologies. [ABSTRACT FROM AUTHOR]

Destination Image can be considered as both, a theoretical and practical tool, to better understand how a destination is perceived in the minds of potential visitors. Given the im- pressive growth of digital sources of tourism-related data in the last decades, methods that exploit this information have been designed to explore this construct. Due to its capacity to emulate human intelligence and its ability to uncover hidden patterns, Artificial Intelligence has captured the attention of the academic and business sectors, for this reason, several ap- proaches from tourism research take advantage of such techniques. However, to date, there is neither sufficient information about what specific methods are being employed nor an eval- uation of their usefulness for the task. In this work, we identify the main techniques, as well as the representations, measurements, and results derived from the computational science perspective related to destination image in tourism studies. As a result, two taxonomies emerged: one related to the group of methods and techniques, and the other pertaining to the results obtained through these particular methodological designs. From our analysis, we found that electronic information is gaining strength as a primary information source, how- ever, our results showed that surveys are still on the top. On the other hand, the preferred techniques for information analysis are based on word frequencies but with a growing trend in the use of neural networks and deep learning techniques. [ABSTRACT FROM AUTHOR]

Investigation of affective and semantic dimensions of words is essential for studying word processing. In this study, we expanded Tse et al.'s (Behav Res Methods 49:1503–1519, 2017; Behav Res Methods 55:4382–4402, 2023) Chinese Lexicon Project by norming five word dimensions (valence, arousal, familiarity, concreteness, and imageability) for over 25,000 two-character Chinese words presented in traditional script. Through regression models that controlled for other variables, we examined the relationships among these dimensions. We included ambiguity, quantified by the standard deviation of the ratings of a given lexical variable across different raters, as separate variables (e.g., valence ambiguity) to explore their connections with other variables. The intensity–ambiguity relationships (i.e., between normed variables and their ambiguities, like valence with valence ambiguity) were also examined. In these analyses with a large pool of words and controlling for other lexical variables, we replicated the asymmetric U-shaped valence–arousal relationship, which was moderated by valence and arousal ambiguities. We also observed a curvilinear relationship between valence and familiarity and between valence and concreteness. Replicating Brainerd et al.'s (J Exp Psychol Gen 150:1476–1499, 2021; J Mem Lang 121:104286, 2021) quadratic intensity–ambiguity relationships, we found that the ambiguity of valence, arousal, concreteness, and imageability decreases as the value of these variables is extremely low or extremely high, although this was not generalized to familiarity. While concreteness and imageability were strongly correlated, they displayed different relationships with arousal, valence, familiarity, and valence ambiguity, suggesting their distinct conceptual nature. These findings further our understanding of the affective and semantic dimensions of two-character Chinese words. The normed values of all these variables can be accessed via https://osf.io/hwkv7. [ABSTRACT FROM AUTHOR]

Lexicon Enhanced Bidirectional Encoder Representations from Transformers (LEBERT) has achieved great success in Chinese Named Entity Recognition (NER). LEBERT performs lexical enhancement with a Lexicon Adapter layer, which facilitates deep lexicon knowledge fusion at the lower layers of BERT. However, this method is likely to introduce noise words and does not consider the possible conflicts between words when fusing lexicon information. To address this issue, we advocate for a novel lexical enhancement method, Sequential Lexicon Enhanced BERT (SLEBERT) for the Chinese NER, which builds sequential lexicon to reduce noise words and resolve the problem of lexical conflict. Compared with LEBERT, it leverages the position encoding of sequential lexicon and adaptive attention mechanism of sequential lexicon to enhance the lexicon feature. Experiments on the four available datasets identified that SLEBERT outperforms other lexical enhancement models in performance and efficiency. [ABSTRACT FROM AUTHOR]

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Sentiment analysis is a review analysis process used to determine whether an opinion is neutral, negative, or positive. Sentiment analysis can be done using lexicon-based or machine learning-based approaches. Lexicon can perform sentiment analysis without training data because it is dictionarybased but performs worse than machine learning. Machine learning can perform well in completing sentiment analysis but requires training data so that the model does not experience underfitting. In the case of sentiment analysis on big data, manual labeling of training data is an inefficient job. Support vector machine (SVM) has the opportunity to be used together with the active learning (AL) method to make small training data but still have good performance. This research proposed a hybrid lexicon and AL-SVM method to complete sentiment analysis on big data tourism. This research used polarity from the valence aware dictionary and sentiment reasoner (VADER) lexicon as a reference for the query by user process from the ALSVM to automate the sentiment analysis process on big data. The experimental results showed that using the hybrid lexicon and AL-SVM increased the sentiment analysis performance compared to the VADER lexicon, SVM, and lexicon SVM, which run separately. [ABSTRACT FROM AUTHOR]

Toddlers with smaller vocabulary than expected for their age are considered late talkers (LT). This study explored the effects of characteristics of words on vocabulary acquisition of 12- to 24-month-old LT children compared with an age matched (AM) and a vocabulary matched (VM) group of typically developing peers. Using the Hebrew-MacArthur-Bates-Communicative Development Inventory questionnaire, words were rated by adults for babiness, concreteness and iconicity and scored for phonological complexity. The effects of the characteristics of the words on their acquisition were examined. Results showed that the vocabulary of the toddlers from all three groups consisted of words that are more relevant to a child's world and experience (babiness), more concrete, more phonologically simple, and more iconic. When LT children were compared to AM children, the characteristics of their vocabulary were different in terms of structure (e.g., iconicity) and meaning (e.g., concreteness), but similar in phonological complexity and babiness. When they were compared to VM peers, the vocabulary characteristics of LT children were similar, though they used words that were less related to babies' world. The parallel patterns found for LT and VM children versus AM children suggest that LT children's vocabularies differ from those of their age-matched peers due to their smaller size and not due to structural differences. The implications for theory and clinic are discussed. [ABSTRACT FROM AUTHOR]

From Darwin through Wittgenstein to contemporary scientific investigations, it has been argued humans tend to view facial expressions through a mentalistic lens. According to this view, when looking at someone's expressive face, we see emotion and are unable to describe the face in behavioral terms, i.e., name the details of facial movements. At the same time, however, a growing body of work shows cultures in fact differ in the degree of importance they attribute to mental states and willingness to discuss them. Is this variation reflected in the linguistic coding of facial expressions? To explore this, we conducted two facial expression naming tasks targeting mental states and facial movements with speakers of Maniq (Austroasiatic, Thailand) and Polish (Indo-European, Poland), representing highly diverse linguistic and cultural settings. We found that, while Polish speakers conformed with the predicted orientation towards mental states, this did not hold for Maniq speakers. The Maniq were instead oriented towards behavioral aspects of faces, naming them more frequently, more precisely, and with higher consensus, compared to the Polish. These differences are carved into the Maniq and Polish lexicons, suggesting diverse cultures exhibit differential specialization in verbalizing expressive faces. [ABSTRACT FROM AUTHOR]

Introduction: Trust is foundational to all social science domains, but to date, there is no unifying theory or consistent measurement basis spanning the social sciences. This research hypothesized that trust forms the basis of an ontology that could unify the social science domains. The proposed ontology comprises a Cartesian plane with axes self-trust and other-trust. Self-trust manifests in dominant behaviors, and other-trust manifests in cooperative behaviors. Both axes are divided into five discrete categories, creating a matrix of 25 cells. All words in the lexicon are allocated into one of these 25 cells. Methods: This research started with an existing 14,000-word lexicon of dominance and affiliation. The lexicon was extended by manually identifying and including socially descriptive words with information regarding self-trust, other-trust, dominance, and cooperation. The taxonomy was optimized using the Gradient Descent machine learning algorithm and commercially curated synonyms and antonyms. The t-test was employed as the objective (or loss) function for Gradient Descent optimization. Word vectors were identified using groups of four words related as synonyms and antonyms. Results: Over 30,000 words were identified and included in the lexicon. The optimization process yielded a t-score of over 1,000. Over 226,000 vectors were identified, such as malevolent-mean-gentle-benevolent. A new form of symmetry was identified between adjectives and verbs with a common root; for example, the words reject and rejected are horizontally reflected. Discussion: The word vectors can create a metrologically compliant basis for psychometric testing. The symmetries provide insight into causes (verbs) and effects (adjectives) in social interactions. These vectors and symmetries offer the social sciences a basis of commonality with natural sciences, enabling unprecedented accuracy and precision in social science measurement. [ABSTRACT FROM AUTHOR]

Phrasal prosody is often viewed as a level of linguistic representation at which the phonetic profile of an utterance varies independently of the lexical items it contains. For example, the same word, when produced at the edges of prosodic phrases, will take longer to produce than when it is produced within the edges of a phrase. Lengthening effects have also been found for words when placed in different syntactic or lexical contexts. Recent evidence suggests that lexico-syntactic information—for example, the global syntactic distributions of words—affects phonetic duration in production, irrespective of other factors. The present study asks whether these lexico-syntactic effects on duration interact with prosodic position within the phrase. Specifically, we ask whether (a) the lexico-syntactic information of a word determines its prosodic position, and (b) whether, beyond any categorical effects on positioning, lexico-syntactic factors affect duration within prosodic positions. We address these questions using the Santa Barbara Corpus of Spoken American English. We operationalize syntactic information as the diversity and the typicality of the syntactic distributions of nouns based on a dependency parse of the British National Corpus. We find that earlier positions in the prosodic phrase generally prefer words with higher syntactic diversity. In addition, diversity and typicality modulate duration more reliably in nonfinal positions. Together, our results point to an early influence of lexico-syntactic considerations on prosodic planning. [ABSTRACT FROM AUTHOR]

This article is dedicated to one of the main lexical cruxes of the Gezer tablet, the expression ʿṣd pšt in the third line of the inscription. I follow the longstanding understanding of the Gezer inscription, according to which pšt refers to flax. The primary innovation of the article lies in the re-evaluation of the cognate Arabic etymology. I argue that the cognate Arabic ʿḍd in both Classical and Modern Arabic does not signify ‘cutting’, as has been suggested in research thus far. Rather, it means ‘upper arm’. I then examine the Mishnaic Hebrew phrase חוצני פשתן ḥoṣne pištan ‘flax bundles’ (m. Pe’ah 6:5) which exhibits a semantic shift: צןֶֹח ḥōṣɛn ‘bosom’ > ‘amount carried under bosom’ > ‘bundle’. Based on the revised Arabic ʿḍd ‘upper arm’ meaning, I propose a semantic parallel: the Gezer inscription’s verbal noun ʿṣd (< ProtoSemitic *ʿ ṣd, ‘upper arm’) has possibly experienced a semantic shift similar to that of פשתן חוצני ḥoṣne pištan ‘flax bundles’ in m. Pe’ah 6:5: ‘upper arm’ > ‘armful of produce’ > ‘bundle’. Accordingly, I suggest ʿṣd pšt means ‘bundling flax’. This suggestion offers a straightforward solution which clarifies etymological data from various Semitic languages, fits within the context of the Gezer inscription and finally resolves the agricultural ‘cutting’ flax difficulty. [ABSTRACT FROM AUTHOR]

To study the application of convolutional neural networks (CNN) in microblog sentiment analysis, a microblog sentiment dictionary is established first. Then, latent Dirichlet allocation (LDA) is proposed for user forwarding sentiment analysis. The sentiment analysis models of CNN and long short-term memory network (LSTM) are established. Experiments are conducted to verify the application effect. The main contributions of this work encompass the establishment of a sentiment lexicon for Weibo, the optimization of two sentiment analysis models, namely CNN and LSTM, as well as the comparison and analysis of the performance of three sentiment analysis approaches: CNN, LSTM, and LDA. The research findings indicate that the CNN model achieves a prediction accuracy of 78.6% and an actual output precision of 79.3%, while the LSTM model attains a prediction accuracy of 83.9% and an actual output precision of 84.9%. The three analysis models all have high sentiment analysis accuracy. Among them, LDA analysis model has the advantages of universality and irreplaceable in text classification, while LSTM analysis model has relatively higher accuracy in sentiment analysis of users forwarding microblog. In short, each sentiment analysis model has its own strengths, and reasonable allocation and use can better classify microblog sentiment. [ABSTRACT FROM AUTHOR]

Research on the relationship between contentious action and news production has often focused on the coverage and framing of specific events, not on the careers of keywords of the protest lexicon itself. However, these keywords play a central role in the negotiation of common understandings of social problems, the legitimation of claims and tactics, and even the shared imaginary of grassroots politics within communities of readers. This article seeks to contribute to this second avenue of media research by studying use of the concept activism and associated subject, activists. I ask how this word, which was a negative term for most of the twentieth century until the introduction and popularization of its modern sense in the 1960s, became a keyword of modern political participation by the public. A conceptual history grounded in insights of distributional semantics and semantic field theory, this article studies patterns of use of 'activist' and 'activism' in two major British quality newspapers, The Guardian and The Times. This comparative approach aims to identify both historical and media-internal factors that contributed to activism becoming a meaningful category in news reporting. Coverage is compared for three episodes of heightened civic contention: the student protests of 1967–1969; Eastern European human rights activism around the Helsinki Accords, 1975–1977; and the industrial strikes of the 1980s, particularly the period around the miner's strike, 1984–1986. • Operationalizes the sociological concept "protest lexicon" to study newspaper communication. • Theorizes the role of keywords in the negotiation of mass media messaging and legitimation. • Provides a conceptual history and reputational career of the keyword "activism". • Combines quantitative and qualitative discourse analysis methodology. [ABSTRACT FROM AUTHOR]

Whenever we read poetry or any literature from different ages, we notice that language changes with time, noticeable in Kashmiri literature from different times. Since considering the poetry of Shaikh–ul–Aalam (RA), the specimen of 14th-century Kashmiri literature, the Language of Shruks is perceived to be varied but still closer to our contemporary language, yet it demands an intense research study. The present work explores the lexical change, a Linguistic-based approach to the analysis of literature, specifically, poetry in Kashmiri language with special reference to kalam-e- Shaikh–ul–Aalam (RA) popularly known as Shruks. Lexical and semantic change technically describes a change in a word's usage or meaning and a generational advancement in people's preferences for particular words or phrases. Language changes most commonly occur through lexical changes, which are much more evident than other kinds of language change. Lexical change takes place at various levels hence diverse kinds of linguistic change the Kashmiri lexicon has undergone through the ages have been explored in this paper. Linguists claim that the lexicon is the most volatile part of language and most likely to undergo change due to superstate influence in a language contact situation. [ABSTRACT FROM AUTHOR]

This study aimed to analyze and elaborate on the dimensions of meaning in the floral lexicons in Javanese proverbs. Employing an ecolinguistic approach, this study focused on the expressions in Javanese proverbs that entail floral lexicons. The data were collected using an uninvolved observation method, as well as documentation, observation, and interviews with relevant informants. The data were then analyzed using the referential identity method, translational identity method, and immediate constituent analysis or distributional technique. Further, data triangulation was also conducted to validate the data with the following steps: discussion, re-checking, and expert consultation. The result shows that the floral lexicons entailed in Javanese traditional proverbs take the form of words and phrases. In word or morpheme form, the lexicons are categorized as nouns and polymorphemic expressions consisting of affix, reduplicative, and compound words. The floral lexicons in Javanese proverbs, in accordance with the cultural values they embody, include depicting attitudes and worldviews; reflecting negative attitudes; being related to strong determination; portraying the relationship between humans and God; and illustrating human interactions with other humans, as well as their connection with nature. The lexicons also represent the thought process and cultural values of Javanese society. [ABSTRACT FROM AUTHOR]

The study aims to unveil how translators deal with confrontational responses through their visibility in translating the diplomatic discourse of China’s diplomatic press conferences. Distinct from past theoretical frameworks, this study is grounded in the impoliteness strategies model (Bousfield, 2008) and the concept of translator’s visibility. The corpus focuses on the English-translated responses using the “challenge” strategy, delivered by the spokesperson Hua Chunying throughout 2020. Findings show that the translator’s visibility is mainly presented at the syntactical and lexical levels, and the discursive level is hardly found. The translation strategy of modulation is used most in syntax and reduction most in lexicon. Additionally, the translator’s visibility by using translation strategies can mediate the degree of confrontation. In maintaining the confrontational tone, the main strategies involve modulation in syntax and reduction in lexicon. Linguistic compression and reduction are employed more commonly in weakening the confrontational implication in lexicon, and reduction in syntax. Linguistic amplification is observed more frequently in the lexicon and modulation is more often used in syntax in strengthening the confrontation. This research sheds light on the dynamic interplay between impoliteness strategies and the translator’s visibility, contributing to a deeper understanding of the translator’s engagement in institutional discourse. [ABSTRACT FROM AUTHOR]

The Jerez (Sherry) vinegars, including Vinagre de Jerez, Reserva, and Gran Reserva, are crafted from Sherry wines and are protected under the Denomination of Origin in Spain. The aim of this study was to (i) characterize the physicochemical properties and composition; (ii) investigate the impact of the aging process on color properties, phenolics, volatile compounds, and sensorial profiles; and (iii) find a marker for tracing the authenticity of non-aged (Apto) and aged Jerez vinegars. The phenolic components were identified through LC-MS/MS, whereas the volatile compounds were examined using the GC-MS/MS technique. As the aging progressed, a decrease was observed in the levels of flavonol and phenolic acids, with anthocyanin components being undetectable in non-aged and aged samples. In the Gran Reserva variety, 2-methylbutyl acetate, acetic acid, and ethanol emerged as the predominant volatile substances. The presence of oaklactone and ethyl butanoate components served as marker substances to authenticate the Gran Reserva. Additionally, alterations in color properties were noted, marked by a decrease in yellow content and an increase in the red component depending on aging. Furthermore, novel sensory descriptors, such as vanilla, clove, woody, and nutty notes, and winy character emerged in the samples with prolonged aging. [ABSTRACT FROM AUTHOR]

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks. [ABSTRACT FROM AUTHOR]

Derivational affixes are not always automatically classified into functional or lexical categories. Although they are treated differently in various approaches, a shared view contends that the status of a suffix is controversial when it forms categorially incoherent words. However, it is debatable even when it forms words of a single category. This study argues that the Bifurcated Lexical Model proposed by Joseph E. Emonds is promising in this respect. With two subcomponents of the lexicon that, respectively, store functional and lexical morphemes, the model allows an affix to behave both as a functional and a lexical morpheme. This study demonstrates that the model can successfully account for the properties of the deverbal noun-forming suffix -ment in Present-Day English. The -ment nouns newly retrieved from the Oxford English Dictionary Online include instances in which -ment attaches to non-verbal elements and converted words. While such nouns may pose potential challenges to the perspective of an affix as a functional morpheme, their existence is not surprising but rather predictable within the model that allows for the flexibility of an affix in the dichotomy between functional and lexical categories. [ABSTRACT FROM AUTHOR]

The Latin terms commonly used to signify "risk" are absent from Thomas Aquinas's economic writings. Instead, Aquinas offers a lexicon of probability, prudence , and danger. This ternary lexicon brings with it a triple universalization of risk: first, a universalization through activity , including the activity of analysis considered as part of economic activity; second, a universalization through the agents , since everyone—the observer, the co-contractors, the prince, and the population—is affected by the risk; and, third, a partial universalization of its definition , since the lexicon indicates a risk that is not yet restricted by calculation, as the modern notion is, although some distinctions are already made by Aquinas. However, the lexicon describes only a risk of loss and does not take into account chance of gain. [ABSTRACT FROM AUTHOR]

Bu araştırmanın amacı, ilköğretim sürecinde zorunlu dersler kapsamında öğrenciler tarafından kullanılan ders kitaplarındaki söz varlığı ile ilgili bir durum analizi ortaya koymaktır. Nitel araştırma yönteminde durum çalışması deseninde yürütülen araştırma, 2018-2019 eğitim öğretim yılı örnekleminde 48 ders kitabı üzerinden gerçekleştirilmiş; doküman incelemesi yoluyla toplanan veriler, içerik analizleri ile değerlendirilmiştir. Ders kitaplarından elde edilen 1.058.434 biçimbirimsel (ham) söz varlığı, anlambirimsel olarak incelenmiş ve araştırmanın amacına uygun olarak literatürde daha önceden belirlenen ölçütler doğrultusunda işlenmiştir. Araştırma sonucunda öğrencilerin zorunlu dersler kapsamında işlenen ders kitaplarında toplam olarak 33.021’İ birbirinden farklı olmak üzere 949.355 söz varlığı unsuruyla karşılaştığı tespit edilmiştir. Bu unsurlardan 864.345’i (20.798 çeşit) temel söz varlığı, 170’i (113 çeşit) atasözü, 16.252’si (2.247 çeşit) deyim, 2.793’ü (636 çeşit) ikileme, 968’i (197 çeşit) kalıp söz, 43.792’si (6.110 çeşit) özel isim, 20.623’ü (2.733 çeşit) sayısal ifade, 412’si (187 çeşit) yabancı dillere ait söz varlığı unsuru olarak belirlenmiştir. Ders kitaplarında yalnızca bir kez kullanılmış olan 11.891 söz varlığı unsuru, ders kitaplarında geçen söz varlığı toplamının yaklaşık olarak %1,25’ini; birden çok tekrar eden 21.129 söz varlığı unsuru (toplamda 937.464 sıklık) ise ders kitaplarında geçen söz varlığı toplamının %98,75’ini oluşturmuştur. Ders kitaplarında en sık geçen ilk 2.002 söz varlığı unsuru ise çalışma ekinde paylaşılmıştır.

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Lexicon projects (LPs) are large-scale data resources in different languages that present behavioral results from visual word recognition tasks. Analyses using LP data in multiple languages provide evidence regarding cross-linguistic differences as well as similarities in visual word recognition. Here we present the first LP in a Semitic language—the Hebrew Lexicon Project (HeLP). HeLP assembled lexical decision (LD) responses to 10,000 Hebrew words and nonwords, and naming responses to a subset of 5000 Hebrew words. We used the large-scale HeLP data to estimate the impact of general predictors (lexicality, frequency, word length, orthographic neighborhood density), and Hebrew-specific predictors (Semitic structure, presence of clitics, phonological entropy) of visual word recognition performance. Our results revealed the typical effects of lexicality and frequency obtained in many languages, but more complex impact of word length and neighborhood density. Considering Hebrew-specific characteristics, HeLP data revealed better recognition of words with a Semitic structure than words that do not conform to it, and a drop in performance for words comprising clitics. These effects varied, however, across LD and naming tasks. Lastly, a significant inhibitory effect of phonological ambiguity was found in both naming and LD. The implications of these findings for understanding reading in a Semitic language are discussed. [ABSTRACT FROM AUTHOR]

Alexis de Tocqueville's Democracy in America (1835, 1840) strongly influenced Western understandings of democracy. The dissemination and reception of Tocqueville's ideas had been studied in various countries, but not in Britain. While some scholars did notice the British influence on Tocqueville, few have asked the reverse question: how did he shape the British political lexicon, especially when it came to the issue of 'democracy'? Drawing on conceptual history and reception studies, this article examines the British uses and interpretations of Democracy in America in periodical reviews and parliamentary debates, with a few incursions into newspapers and monographs, from 1835 to 1885. I underline the dual nature of the British response to Tocqueville's work. Whereas in France and the USA, Tocqueville was identified as a liberal and read as such, in the British Isles, both conservatives and progressives tried to claim him as one of their own. The former argued that his book demonstrated the dangers of 'democracy', while for the latter it analysed both the challenges and benefits of the democratic age. Through this study of this double claiming of Tocqueville, I illustrate larger trends in the uses of 'democracy' and plea for an historical and reflexive understanding of current debates. [ABSTRACT FROM AUTHOR]

Prompt-tuning has emerged as a promising approach for improving the performance of classification tasks by converting them into masked language modeling problems through the insertion of text templates. Despite its considerable success, applying this approach to relation extraction is challenging. Predicting the relation, often expressed as a specific word or phrase between two entities, usually requires creating mappings from these terms to an existing lexicon and introducing extra learnable parameters. This can lead to a decrease in coherence between the pre-training task and fine-tuning. To address this issue, we propose a novel method for prompt-tuning in relation extraction, aiming to enhance the coherence between fine-tuning and pre-training tasks. Specifically, we avoid the need for a suitable relation word by converting the relation into relational semantic keywords, which are representative phrases that encapsulate the essence of the relation. Moreover, we employ a composite loss function that optimizes the model at both token and relation levels. Our approach incorporates the masked language modeling (MLM) loss and the entity pair constraint loss for predicted tokens. For relation level optimization, we use both the cross-entropy loss and TransE. Extensive experimental results on four datasets demonstrate that our method significantly improves performance in relation extraction tasks. The results show an average improvement of approximately 1.6 points in F1 metrics compared to the current state-of-the-art model. Codes are released at https://github.com/12138yx/TCohPrompt. [ABSTRACT FROM AUTHOR]

The main subject of this paper is the lexical-semantic classification of water-related lexicon in the novel Most (Bridge) by Jasmina Musabegović. We will explain the stylistic expressiveness of selected water-related lexemes, as well as the way the author constructs her identity and the identities of her characters through the conceptualization of the experience of water. The water lexics are analysed as dominant in relation to other types of words and classified into eleven lexical-semantic fields. Total of 79 water nouns have been registered. As expected, the most common water-related lexemes are ‘voda’ and ‘most’. Three water nouns are described: voda, most and tihanj, and it is concluded that lexeme ‘water’ has an associative and symbolic role; lexeme ‘most’, as a strong position of the text, since it is also a title of the novel, represents change, a path and growing up. The nonce word tihanj has a specific cultural and linguistic meaning in the context of the novel.

Text mining has led to growth in sentiment analysis (SA) across various research disciplines. The pandemic has provided a unique and special context for analysing students’ written expression. We utilised comments from a survey conducted during the pandemic to create a corpus for SA. The corpus comprises 25,197 words extracted from over 600 comments in Spanish, collected during a survey that lasted around 20 days. We aim to detect sentiments and emotions from this corpus using SA. However, some essential and little-discussed issues in literature should be addressed, such as its relationship with post-cognitivist theory. This paper uses the post-cognitivist approach to analyse emotions and sentiments through SA with the Spanish lexicon in the economics of education. Literature in this area needs further development, especially in Spanish. The article shows that the emotions and sentiments of students in challenging situations can be identified through a corpus of student comments. However, specific elements should be considered while interpreting emotions and sentiments within the framework of post-cognitivism methodologies. Recognising that the human experience is a complex interaction, it is essential to consider the emotional nuances within the context in which they develop. Addressing this issue from the post-cognitivist approach is one of several ways to carry out this task. Using SA and emotions to analyse a text corpus is still helpful for researchers who follow the post-cognitivist approach. However, combining this technique with other qualitative and in-depth computational methods is essential to fully understanding the emotional experiences within their respective contexts.

The intersection of two lexical-semantic fields “mentality” and “physiology” in the poetic texts of Igor-Severyanin is studied. The relevance of the research is determined by the fact that within the framework of modern anthropocentric approach and increased attention to the mental sphere lexical semantics is investigated, lexical-semantic fields, their intersections, semantic shifts are considered, actual problems of linguistic poetics are touched upon. The aim of the work is to explore the intersection of lexical-semantic fields “mentality” and “physiology” in the language of Severyanin's poems. The material includes lexemes of the mental sphere from Igor-Severyanin's poetic texts. While determining the usual meaning of these lexemes, the authors used the materials of “Explanatory Dictionary of the Russian Language” by S.I. Ozhegov. The methods of description, comparison, contrast, lexical-semantic and contextual analysis, and continuous sampling were applied; the lexical-semantic field - a generally accepted form of representing lexical units by their meaning - was used to structure the vocabulary. The intersections of the lexical-semantic fields “mentality” and “physiology” in Igor-Severyanin's texts, revealed in the research, are primarily due to the inseparable connection between the inner world of man and his bodily organs of sense. The language fixes stable expressions indicating the possibility of comprehending the human consciousness through analyzing the signals of his body. Words used to denote phenomena and processes related to the body, health, gestures, etc., are reinterpreted in poetic language and receive new semantics, which allows to refer these new lexical-semantic units to the lexical-semantic field of mentality. In Igor-Severyanin's poems the inner world of the lyrical subject dominates, which is reflected in the author's word usage. Even words and expressions with the semantics of physiology shift in their meanings, acquiring the semantics of mentality, at the same time thoughts and feelings are endowed with the author's positive evaluation. The prospects of the research are connected primarily with further study of the mental sphere in the Russian linguistic lexical-semantic system, its dynamics, as well as with the analysis of semantic shifts in the poetic language vocabulary.

This study unveils insights into the spatial and social distribution of Portuguese lexical forms, or lexies, pertaining to urban life in the interior of São Paulo State. These lexies were collected through oral responses from eighty participants, elicited by nine questions addressing variants for terms such as ‘traffic lights’, ‘speed bump’, ‘sidewalk’, ‘curb’, ‘traffic circle’, ‘plot of land’, ‘city bus’, ‘intercity bus’, and ‘a small grocery store where one can also drink liquor at a counter’. The analysis examines whether these lexies establish themselves as predominant norms, indicated by both consistent spatial distribution and a relative frequency exceeding 50% across the localities surveyed. Out of the nine cases studied, eight meet this criterion, with the following overall relative frequencies: ‘semáforo’ 53%; ‘lombada’ 96%; ‘calçada’ 94%; ‘guia’ 84%; ‘rotatória’ 74%; ‘terreno (baldio)’ 79%; ‘ônibus’ (in the sense of ‘city bus’) 71%; ‘ônibus’ (in the sense of ‘intercity bus’) 51%. The ninth variant, ‘bar,’ nearly meets this standard as well, with a relative frequency of 41% just falling short of the minimum threshold.