Semantic Categories Research Articles

Does targeted memory reactivation during slow-wave sleep and rapid eye movement sleep have differential effects on mnemonic discrimination and generalization?

Targeted memory reactivation (TMR), or the presentation of learning-related cues during sleep, has been shown to benefit memory consolidation for specific memory traces when applied during non-rapid eye movement (NREM) sleep. Prior studies suggest that TMR during rapid eye movement (REM) sleep may play a role in memory generalization processes, but evidence remains scarce. We tested the hypothesis that TMR exerts a differential effect on distinct mnemonic processes as a function of the sleep state (REM vs. NREM) in which TMR is delivered. Mnemonic discrimination and generalization of semantic categories were investigated using an adapted version of the Mnemonic Similarity Task, before and after sleep. Forty-eight participants encoded pictures from eight semantic categories, each associated with a sound. In the pre-sleep immediate test, they had to discriminate "old" (targets) from "similar" (lures) or "new" (foils) pictures. During sleep, half of the sounds were replayed in slow wave sleep (SWS) or REM sleep. Recognition, discrimination, and generalization memory indices were tested in the morning. These indices did not differ between SWS and REM TMR groups or reactivated and non-reactivated item categories. Additional results suggest a positive effect of TMR on performance for highly similar items mostly relying on mnemonic discrimination processes. During sleep, EEG activity after cue presentation increased in the delta-theta and sigma band in the SWS group, and in the beta band in the REM TMR group. These results do not support the hypothesis of differential processing of novel memory traces when TMR is administered in distinctive physiological sleep states.

Обработка зрительной вербальной информации в условиях чтения и категоризации слов в составе одного согласованного словосочетания

The purpose of this study was to identify neurophysiological differences in information processing by the brain during passive reading and semantic categorization of individual words within a phrase. Materials and methods. The research involved 25 natural science students of Nizhny Novgorod universities. In the course of the experiment, the subjects were presented with word combinations consisting of an attribute and a noun on a computer monitor. In the first series of presentations, the task was to classify the attribute (target condition) and read the noun (non-target condition), in the second, to classify the noun and read the attribute. This allowed us to assess the restructuring of information processing when changing the task within a phrase, as well as to compare the processing of the word in the first and second positions in the target and non-target conditions. Using the 21-channel Neuron-Spectrum-4/VPM electroencephalograph (Neurosoft, Russia), event-related potentials (ERPs) obtained during the response to presented stimuli were recorded. Results. No differences were observed in attribute processing between the target and non-target conditions when tasks changed. For nouns, differences were revealed in ERP components between the target and non-target conditions. There was a more pronounced peak of the P200 component, a less pronounced peak of the N400 and a late negative wave of the N700 component during the categorization of nouns compared to their passive reading. Thus, when performing different tasks within a phrase, the brain changes the nature of verbal information processing, which is manifested in strengthening early lexical access, followed by the facilitation of lexico-semantic processing and activation of cognitive control mechanisms at the latest stages during the categorization of the second word in a phrase (noun) compared to its passive reading.

Myth of mankind and the representation of people in Late 18th-century British dictionaries of trade and commerce

The investigation aims at analysing the two general notions of mankind and people in two lexicographic reference works on trade and commerce published in London in the 1750s: Rolt’s A New Dictionary of Trade and Commerce (1756) and Postlethwayt’s The Universal Dictionary of Trade and Commerce (1757). An overview of the same notions in universal dictionaries of arts and sciences (cfr. References, Primary Sources), issued before and after ND and UD, will also be of help to define a more general background. Starting from the key words mankind and people, their frequency and relevance in the two dictionaries, the investigation is widened to include related terms (e.g. multiword expressions and language patterns in which mankind and people occur) and semantic categories (e.g. specific contexts of use: social condition, social values, demography, identity and citizenship, etc.). The many entries, cross-references, and the various lexical clusters reveal a complex lexicographic and lexicological network. This network, beyond providing information and contents on specific topics, ultimately emphasises the hierarchical organising principle at the basis of Late Modern British society: in particular, people unfolds into a myriad of ‘entities’. Dictionaries of trade and commerce are plentiful sources of knowledge, from general concepts and notions to more specific contents, but also powerful ideological tools.

Saliency Detection Based on Multiple-Level Feature Learning.

Finding the most interesting areas of an image is the aim of saliency detection. Conventional methods based on low-level features rely on biological cues like texture and color. These methods, however, have trouble with processing complicated or low-contrast images. In this paper, we introduce a deep neural network-based saliency detection method. First, using semantic segmentation, we construct a pixel-level model that gives each pixel a saliency value depending on its semantic category. Next, we create a region feature model by combining both hand-crafted and deep features, which extracts and fuses the local and global information of each superpixel region. Third, we combine the results from the previous two steps, along with the over-segmented superpixel images and the original images, to construct a multi-level feature model. We feed the model into a deep convolutional network, which generates the final saliency map by learning to integrate the macro and micro information based on the pixels and superpixels. We assess our method on five benchmark datasets and contrast it against 14 state-of-the-art saliency detection algorithms. According to the experimental results, our method performs better than the other methods in terms of F-measure, precision, recall, and runtime. Additionally, we analyze the limitations of our method and propose potential future developments.

의미 운율의 정도성 연구

This paper aims to explore the need for expanding the scope and methodology of semantic prosody analysis. First, the scope of the co-occurrence relation of the node was expanded to include not only adverbs, but also subjects and words that modify them. The ‘category’ of the evaluative meaning of co-occurring words was analyzed by considering the context, and the ‘degree’ was measured using the KNU Korean Sentiment Lexicon. And the realization type of semantic prosody in the node was compared and analyzed by dividing it into type and co-occurrence relation. The results of the categorical and degree-based approaches to semantic prosody were compared. As a result of the analysis, first, the type of semantic prosody realization and usage patterns of co-occurring words were different depending on the node. Second, the usage patterns of subjects and adverbs were most similar to ‘giinhada-birotada’ and ‘birotada-oda’. Third, nodes with the same semantic prosody category could be compared more precisely by analyzing the degree of semantic prosody. This showed that there is a need to expand the scope of co-occurrence relation and methodology of analysis when analyzing semantic prosody.

A Literature Review of Language Characteristics in Patients with Vascular Dementia

Vascular dementia may present with language disorders due to damage to frontal subcortical circuits. This study aimed to review recent studies of language abilities in patients with vascular dementia. Sixty-nine studies were classified according to language modalities (spontaneous speech, auditory comprehension, repetition and naming). First, spontaneous speech was impaired in the ability to describe pictures, objects and in the ability to convey information in discourse. Second, the auditory comprehension was impaired in word comprehension, sentence comprehension, paragraph comprehension, and speech act comprehension tasks. Third, repetition was preserved in word-level tasks but impaired at the sentence level. Fourth, the naming performance was impaired in a generative naming task (phonemic fluency) rather than in a confrontational naming task. Therefore, when summarizing the results between patient groups, in spontaneous speech, to convey information through highly informative utterances was relatively good in vascular dementia (VaD), but the number of utterances was lower than dementia of Alzheimer’s type (DAT). In auditory comprehension, VaD’s performance was good in the task of word comprehension and speech act comprehension tasks. It is organized as in repetition, VaD’s performance was relatively good in the sentence-level task, and in naming, VaD’s performance was reported to be better than DAT in the oppositional naming and generative naming semantic category tasks. In the generative naming phoneme category, opposite result was reported. Further studies on the language abilities of patients with vascular dementia needs to consider factors such as subtype and language modality. Future studies should be evenly distributed across all language domains.

Design and Evaluation of English Vocabulary Learning Aids Based on Word Vector Modelling

English vocabulary learning aids based on word vector modeling involve creating tools that leverage advanced techniques to enhance vocabulary acquisition. Word vector modeling, often using methods like Word2Vec or GloVe, represents words as high-dimensional vectors capturing semantic relationships. These models can power vocabulary learning aids by offering context-based word suggestions, personalized word quizzes, or interactive visualizations of word associations.. The paper introduces the Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework, a novel approach aimed at enhancing vocabulary learning and classification tasks. With word vector modeling and spiking neural networks, HSV-DL offers a sophisticated methodology for accurately categorizing vocabulary words into their respective semantic categories. The Hierarchical Spiking Vocabulary Deep Learning (HSV-DL) framework introduces novel methodologies for vocabulary learning and classification tasks, achieving outstanding performance metrics. Experimental results demonstrate high accuracy (95%), precision (96%), recall (94%), and F1-score (95%) in categorizing vocabulary words into their semantic categories. Moreover, HSV-DL exhibits robustness to noise and efficient resource utilization, showcasing its potential for real-world applications in natural language processing.

Artificial intelligence in judicial adjudication: Semantic biasness classification and identification in legal judgement (SBCILJ)

History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.

Determining a subset of discrete emotion-evoking images from the international affective picture system in a Turkish sample

The International Affective Picture System (IAPS), widely utilized in emotion research, is based on a dimensional approach. It includes 1196 colored static images depicting different semantic categories, with valence, arousal, and dominance ratings determined for each. However, the specific discrete emotions evoked by these images within the IAPS has remained unclear. Therefore, numerous investigations have been conducted in different cultural contexts to address this matter. This study aimed to determine a subset of images from the IAPS that elicit discrete emotions. To achieve this goal, an image was selected for each semantic category within the IAPS, employing specific criteria, and the elicited discrete emotions were subsequently examined in a Turkish sample. Additionally, valence ratings for these images were obtained within the Turkish culture, facilitating cross-cultural comparisons. Sixty Turkish students (43 female) aged between 19-25 participated in this study. The participants rated the valence, discrete emotion category, and the intensity of the determined discrete emotion (1 = none; 9 = very strongly) for 231 selected images from the IAPS, respectively. Valence ratings were obtained using the paper-pencil version of the Self-Assessment Manikin. Accordingly, 76 images, of which intensity ratings above six were classified into a single discrete emotion with the agreement of 70% and above among the participants. Furthermore, the obtained data were compared with the results of studies conducted in different cultural settings to explore potential cultural differences. Overall, the results highlighted the importance of selecting culture-specific stimuli in emotion studies.

Episodic long-term memory formation during slow-wave sleep.

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

Episodic long-term memory formation during slow-wave sleep

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words’ linguistic processing raised neural complexity. The words’ semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

On the Theoretically Possible Number of Cases in Natural Language

Introduction. The case, which is a semantic and grammatical category, appears in natural languages in the most unexpected way. A word or phrase that has a substantive unit at its core receives an inflection (internal or external) in one case or another, which can be expressed both explicitly and implicitly, and has a grammatical meaning. In this case, inflection can be accompanied by a preposition that has a lexical meaning. These case indicators, morphological and syntactic, organize the main or secondary substantive part of the sentence, i.e. are related to the category of space. The number of cases today is still the subject of scientific debate. Methodology and sources. The present study is based on analizing the views of various scientists on the category of case, with special attention to the theory of L. Hjelmslev. According to this theory, the maximum possible number of cases in natural language is 216. In order to provide a purely formal calculation of the possible number of cases, which would be at the same time independent on the earlier views, the article represents the substantive part of the sentence as a semifinitive multiplied by a specifier (proto-specifier). These statements are prescribed by the binomiality idea, having been developed since 1993 at ETU. Results and discussion. The generally accepted division of sentence parts into main and secondary ones leads us to dividing all possible cases into direct cases (for subject) and indirect cases (for secondary substantive parts). Thus, the direct case can be obtained by a simple transformation from any indirect case, i.e. the space specifier goes back to the spatial proto-specifier. The space proto-specifier represents external space added to substantive mass. The substantive semifinitive is a substantive mass multiplied by internal space. As the binomiality idea prescribes, substantive masses can be in four states, while substantive semifinitives can be in six states. Thus, the number of possible case forms should be equal to 24. Conclusion. According to classical physics, space is three-dimensional, so grammatical space, both external and internal, should also, obviously, be considered three-dimensional. This means that four states of substantive mass and six states of substantive semifinitives can be in each of the three dimensions. Thus, the maximum possible number of cases may indeed reach 216. It should be noted that L. Hjelmslev derived the number 216 in a completely different way, by dint of much deeper reasoning. In the future, it would be interesting to compare L. Hjelmslev’s binary oppositions with the constituents of substantive parts of sentence.

Category of learning modality in English texts

In the literature of linguistics, modality is a semantic category that expresses the attitude of the speaker to the content of his expression, the target designation of the speech, the relationship of the content of the expression with reality. The category of modality is a linguistic universal and stands out as one of the main categories of language. This article examines the category of modality, one of the most popular and interesting areas in modern English grammar. Different types of modality are defined, examples and features of working of modal words in English texts are given, detailed explanation is given to the presented usage examples. The relevance of the research is related to the necessity of a comprehensive review of the category of modality from the point of view of the theoretical grammar of the modern English language, the importance of systematizing modal words, and determining their status in linguistics.

Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% of the Earth's biodiversity. However, we are witnessing an average loss of 480 ha of forest every hour because of destructive wildfires spreading across the globe. To effectively mitigate the threat of wildfires, it is crucial to devise precise and dependable approaches for forecasting fire dynamics and formulating efficient fire management strategies, such as the utilisation of fuel models.The objective of this study was to enhance forest fuel classification that considers only structural information, such as the Prometheus model, by integrating data on the fire responses of various tree species and other vegetation elements, such as ground litter and shrubs. This distinction can be achieved using multispectral (MS) Light Detection and Ranging (LiDAR) data in mixed forests. The methodology involves a novel approach in semantic classifications of forests by generating synthetic data with semantic labels regarding fire responses and reflectance information at different spectral bands, as a real MS scanner device would detect. Forests, which are highly intricate environments, present challenges in accurately classifying point clouds. To address this complexity, a deep learning (DL) model for semantic classification was trained on synthetic point clouds in different formats to achieve the best performance when leveraging MS data.Forest plots in the study region were scanned using different Terrestrial Laser Scanning sensors at wavelengths of 905 and 1550 nm. Subsequently, an interpolation process was applied to generate the MS point clouds of each plot, and the trained DL model was applied to classify them. These classifications surpassed the average thresholds of 90% and 75% for accuracy and intersection over union, respectively, resulting in a more precise categorisation of fuel models based on the distinct responses of forest elements to fire. The results of this study reveal the potential of MS LiDAR data and DL classification models for improving fuel model retrieval in forest ecosystems and enhancing wildfire management efforts.

Terminologization of Colouronyms of Metal and Mineral Etymological Origin: The Evolutionary Aspect

This paper is devoted to the study of French colouronyms and their terminologization in different subtypes of scientific discourse. The research is based on colouronyms of mineral origin “(jaune / vert / brun / rouge d’) andalousite” and “quartz (blanc / rose),” as well as those of metallic origin – “jaune d’or” and “jaune / vert de chrome.” The authors carry out an etymological analysis of all the above-mentioned lexical units and consider the main tendencies of the terminologization of words with the semantics of colour in French scientific discourse. The peculiarities of the terminologization of colouronyms of mineral origin are analysed in the chemistry and geology discourse subtypes, whereas trends in the use of colouronyms of mineral origin are presented in the chemistry and physics discourse subtypes.The aim of this paper is to analyse the evolutionary tendencies of the terminologization of French colouronyms in the subtypes of scientific discourse to describe their common and diverging properties at different levels, i.e. structural-semantic, morphological, and syntactic.The empirical material for the research comprises written scientific texts on chemistry(the Annales de chimie et de physique electronic archive of scientific journals), geology (the Planète-Terre electronic archive of scientific journals), and physics (the Futura Sciences electronic archive of scientific journals).The research methods employed include etymological, component, structural-semantic, morphological, and syntactic analyses.The terminologization of colouronyms is a long-term continuous evolutionary discursive process that can be observed in French scientific discourse from the nineteenth century until the present day. The authors investigate the evolutionary tendencies of colouronyms as emerging semantics of colour in lexemes that have not had it before, the development of new structural-semantic models, the increasing numbers of morphological categories of colour terms, the acquisition of new syntactic roles, and the gradual process of the semantic transformation of colouronyms. The transition of colouronyms from one subtype of scientific discourse into another while preserving the structural-syntactic forms and morphological and semantic categories indicates the correlation between the discursive subtypes and the convergence of their functional properties at different levels of the language system. Despite this interdiscursive connectedness, each of the subtypes manifests distinct boundaries, which can be revealed through the study of the semantic specificity of colouronyms and their terminologization in scientific discourse.Transl.: Volkova M.V. 2022. Terminologization of Colouronyms of Metal and Mineral Etymological Origin: The Evolutionary Aspect. Professional Discourse & Communication. 4(2). P. 45–65. DOI: https://doi.org/10.24833/2687-0126-2022-4-2-45-65

“Cansada, agobiada, frustrada”: Desafíos emocionales de docentes de inglés en primaria durante la pandemia: Estudio de caso en San Carlos, Costa Rica

Introduction. COVID-19’s health crisis put a hold on all educational processes worldwide. This was not the exception for Costa Rica at the Ministry of Education. In March 2020, it was proposed that distance education was going to be put into practice. Purpose. This paper focuses on the feelings the participants communicated during the study. This was part of a principal investigation that used a case study methodology based on inductive qualitative research. It had the purpose of describing the perceptions of four English primary school teachers from the Dirección Regional de Educación San Carlos (San Carlos Regional Education Office) regarding distance education during the sanitary emergency of COVID-19. Methodology. Data were collected through in-depth interviews, lesson plans, and video recordings of pedagogical mediation and professional contexts. The data were analyzed using semantic categories from content analysis (Hatch, 2002) and the Web Qualitative Data Analysis (webQDA) software (Costa et al., 2019). Results. Results suggest that teachers reported experiencing a range of emotions before and during the pandemic, with the majority being negative in nature. Among the most notable were uncertainty, stress, confusion, overwork, a sense of difficulty, sadness, and worry. Other feelings included discrimination, impotence, and disrespect towards the teachers and the families’ specific contexts. This study presents the teachers from their human dimension as professionals who endured the pandemic within their own educational contexts. The study also shows the importance of effective communication and taking into consideration psychological support to strengthen the emotional integrity of the teachers.

Niemieckie i polskie nazwy szkół wyższych w ujęciu języko- i przekładoznawczym (XX i XXI wiek)

GERMAN AND POLISH NAMES OF HIGHER SCHOOLS FROM A LINGUISTIC AND TRANSLATIONAL PERSPECTIVE (20TH AND 21ST CENTURIES) The present paper is an attempt to describe the nomination usus (namegiving linguistic custom) regarding higher schools of the German-speaking area on the one hand, and of the Polish-speaking one on the other. The specific semantic categories of nominal components correspond to grammatical realisations, which are subject to certain structural constraints, as well as exhibit more and less typical forms. Informed by such observations – initiated by Jan Iluk’s overview paper [2000] and refined by an analysis of much broader material, encompassing Austrian and Swiss names as well – I tried to show the name-giving possibilities and tendencies in this field in both German and Polish, mainly with a view to a translation-related application of such contrastive analysis. The findings presented here, based on the material of a considerable number of higher school names in the examined area and combined with the presentation of about 100 German and some 45 Polish examples (provided with translations), should help both in evaluating already published equivalents, hardly ever official in the case of German, and proposing one’s own ones – also because of the non-existence of given equivalents in publications, be it only Internet-based ones.

The lexical expression of CIRCUMSTANCE and DEGREE in old English

This paper explores the distribution of the affixal material and lexical meaning within the semantic categories ‘circumstance’ and ‘degree’. The analysis is confined to the predicates belonging to the four major lexical categories, namely verbs, nouns, adjectives, and adverbs, that have been formed either prefixally or suffixally. Special attention has been paid to affixal polysemy, which applies when a given form can be bound to more than one semantic rule. In this regard, it must be noted that both categories present a significant degree of affixal overlapping that is confirmed in the polysemous character of a group of affixes, which include for-, fore-, forð-, mid-, ofer-, on-, sin-, ūp- and ūt-. The analysis has also revealed that ‘circumstance’ is mainly characterized by affixes conveying location, whereas ‘degree’ is mainly defined in terms of affixes expressing intensity. On the whole, the study casts lights on the degree of compatibility among affixes and functions and on the patterns of recategorization that arise as a result of affixal processes.

The Pragmatic Motivation of Atypical English Way into/to Construction

The English way construction exhibits distinct constructional meaning and semantic features; however, the prepositional slot in this construction has not received sufficient attention, and there remains a lack of comparative research regarding different prepositions in this field. This study set out to examine the semantic classification, mapping features, cognitive factors in the subcategory English way into/to construction, and to improve the algorithm of ΔPratio. The research adopted a bottom-up corpus-driven approach based on the BNC and used HAC (Hierarchical Cluster Analysis) to explore the semantic categories of the verb collexemes and compare the lexical richness in these two constructions. The results revealed that at the level of linguistic structure, coercion and metaphor, as well as family resemblance, served as legitimization methods for verb entry into the construction, while the principles of economy and expressivity disclosed the intentions and motivations behind the semantic extensions of constructions at the pragmatic level. Additionally, this research found that the latter two principles were competitive in the interpretation process of way into/to construction, providing insights into understanding the psychological and social factors in language communication.

A corpus‐based analysis of adjective amplification in Hong Kong, Indian and Philippine English

AbstractThis study considers a corpus‐based approach for analysing adjective amplifier systems (very, really, so, etc.) in Hong Kong English (HKE), Indian Englsih (IndE) and Philippine English (PhiE) based on data from the International Corpus of English. The current study adds to existing research by providing insights into the understudied adjective amplifier systems of Asian Englishes. The study describes the amplifier systems in HKE, IndE and PhiE, and it ascertains if these amplifier systems are undergoing change. In the latter context, the analysis assesses if really or so is replacing very as the dominant adjective amplifier in these varieties—a trend shown to be at work in Inner Circle varieties of English. The results of Conditional Inference Trees show that the amplifier systems of HKE and IndE are very stable and amplifier choice in these varieties is determined predominately by intra‐linguistics factors (adjective type, syntactic context and semantic category). In contrast, the amplifier system of PhiE shows notable signs of ongoing change, which is driven predominately by social factors (age and gender of speakers). The results indicate that during stasis and initial stages of change, language‐internal factors determine amplifier choice, while social factors become more important once changes have reached the midrange. The paper argues that once certain variants (really and so) gain social meaning, it is this social meaning (the association with specific social groups) that drives and accelerates change as speakers want to associate with social groups that carry covert prestige.