Specific Semantics Research Articles

Towards the sociology of pets—Social functions of animals in contemporary societies observed from systems theoretical perspective

AbstractThis paper advocates for introducing sociology of pets as a particular field of research as the response of ever growing social relevance of pets in contemporary societies. Though other theoretical approaches are suitable for tackling these complex issues, the article offers viewpoint from Luhmann's social systems theory. Based on Luhmann's writings on ecological communication, the article departs from the system/environment distinction and conceives pets as being part of a society's environment that attained resonance of a social system. Further, entry of pets into a social system is interpreted through differentiation theorem arguing that pets perform highly relevant social functions for all functional subsystems of a society by contributing to their autopoietic reproduction. This is primarily accomplished through communication about pets, emerging specific semantics, usage of binary codes specific for different subsystems, correspondence to their mediums, programs and functions. The major part of work is presenting relations of pets to 10 functional subsystems of a society, as this delineates scope of empirical phenomena of potential interest for sociology of pets. These processes might be observed at systemic, organizational and interaction levels. Despite possessing multiple merits, also, certain limits of Luhmann's account of pets are unfolded, such as incapability to hear authentic voices of animals, but rather distorted semantic codes emerging from consciousness of human psychic systems, afterwards structurally coupled with communication as a medium of social systems. At this point, social systems theory might be complemented by theory of interspecies communication, as taking into consideration other theoretical approaches anchored at ontologies pertinent to include non‐human agents is invocated. The tentative outcome of this introductory discussion is that apparent process of anthro‐morphosization of animals is taking place while the question of relation between humans–humans in the context of building just societies of the 21st century still remains open.

Рецензия на монографию «Мусуков Б.А.-К. Формально-семантическая парадигма колора-тивной лексики в тюркских языках. – Нальчик: Binding2016, 2023. – 163 с.»

The content of B.A.-K. Musukov’s monograph “The formal semantic paradigm of color vocabu-lary in Turkic languages,” which was published in Nalchik in 2023, is given and studied. The re-viewer characterizes the two chapters of the book consistently by surveying the lexico-semantic and structural aspects of a considerable layer of vocabulary from Turkic languages representing nominations of various colors. The author’s use of the comparative historical technique in the ex-amination of colorants is emphasized. A multivector analysis of the adjectival colorative vocabu-lary led the author to the conclusion that coloronyms differ from other categories of adjectival words in their specific semantics and individual grammatical features, as well as in ethnocultural terms, which are influenced not only by the Turkic language system but also by their speakers’ cultural and historical experiences. It is suggested that the author adopt the anthropocentric para-digm of modern humanities and linguistics to study the issue in question in future research.

Linear and nonlinear processing of Hebrew templatic words: the role of metalinguistic awareness

Abstract While universal linguistic theories advocate linear processing of words across languages, psycholinguistic research of Semitic templatic words supports the nonlinear processing into the root and template, mainly due to semantic specifications related to the root and the morphological awareness needed in the tasks. The present study examined whether the root and template affect the word processing of Hebrew native speakers due to metalinguistic awareness regardless of semantics. We designed an auditory rhyme judgment task, a phonological awareness test that requires linear processing and reduces semantics. The task included Hebrew CVCVC templatic word pairs comprising varying root and template phonemes, transposed-phoneme roots emphasizing phonological cooccurrence restrictions, and identical phoneme roots accentuating lexical-syntactic information pronounced in the vocalic melody templates. Findings revealed low accuracy rates in rhyming pairs, particularly those with accentuated linguistic information, indicating distraction from linear processing. However, the accuracy distributions among participants and between and within the stimulus types showed that linear processing also occurred. These results suggest that both linear and nonlinear processing modes are accessible to native Hebrew speakers. The study aligns with models of the mental lexicon proposing dynamic language processing influenced by both linguistic and non-linguistic factors, highlighting the idiosyncratic nature of word processing strategies.

Multifaceted formal methods and their interdisciplinary role — From the cathedral of ‘components as coalgebras’ to the HCI context and the open source software bazaar

In this article we revisit the history of formal methods with a focus on important aspects that contribute to their interdisciplinary role. We consider: the variability of mathematical representation techniques on which the theoretical foundations of formal methods are based; formal methods multidisciplinarity; their capability to serve at a meta-level in providing the semantics of programming languages, specification and modelling languages as well as higher-level and domain-specific formal notations; and, finally, how some of these higher-level and domain-specific notations may be lifted at an interdisciplinary level.Within this historical review, we are inspired by Luís Barbosa's “components as coalgebras” approach in seeing that the duality data-process is underlying all those aspects of formal methods. We also see that such a duality may not only be expressed in universal terms within category theory, but may also be characterised in practical terms and focused applications by two distinct logic paradigms, equational logic for the data and rewriting logic for the process, by two modelling directions, forward process definitions and backward data-driven process transformations, and by the distinction between syntax, defined by the data structures, and semantics, provided by rewrite rules. We use the Maude modelling language to illustrate the application of the data-process duality. In fact, Maude use equational logic to define data types and rewriting logic to express system evolution. Illustrative examples are from the areas of cognitive science and human-computer interaction (HCI).We then define a data-driven model transformation, which we call elaborative mining, which adopts a backward perspective to recover a behaviour that was observed in real life but was not predicted forward by the original model. Finally, we see how the “open source software bazaar”, which is a metaphor for the apparently chaotic open source development process, offers us a big data context to lift the driving process for model transformation from deterministic to statistical.

Towards declarative comparabilities: Application to functional dependencies

In real life, data are often of poor quality as a result, for instance, of uncertainty, mismeasurements, missing values or bad inputs. This issue hampers an implicit yet crucial operation of every database management system: equality testing. Indeed, equality is, in the end, a context-dependent operation with a plethora of interpretations. In practice, the treatment of different types of equality is left to programmers, who have to struggle with those interpretations in their code. We introduce a lattice-based declarative framework to address this problem. It allows specification of numerous semantics for equality at a high level of abstraction. To go beyond tuple equality, we study functional dependencies (FDs) in the light of our framework. First, we define abstract FDs, generalizing classical FDs. These lead to the consideration of particular interpretations of equality: realities. Building upon realities and possible/certain answers, we introduce possible/certain FDs and give some complexity results.

Functional and semantic specificity of the elements of the “poverty” microfield in the Russian language

Functional and semantic specificity of the elements of the “poverty” microfield in the Russian language

Knowledge Graph Reasoning via Dynamic Subgraph Attention with Low Resource Computation

Knowledge graphs (KGs) suffer from inherent incompleteness, which has spurred research into knowledge graph reasoning (KGR), i.e., ways to infer missing facts based on existing triples. The most prevalent approaches employ a static mechanism for entity representation across the entire graph, sharing entity embeddings for different query relations. Nevertheless, these static entity embeddings fail to capture specific semantics for various query scenarios, resulting in inaccurate entity representations and susceptibility to reasoning errors. Furthermore, the whole graph learning style requires substantial computational resources, especially since KGs are often of large-scale. Consequently, these methods are impractical in low-resource environments. To address these issues, we devise a framework called dynamic subgraph attention (DSA), which learns dynamic entity embeddings for different query relations across subgraphs. In our approach, by utilizing multi-hop path history information obtained through path-based learning, we guide a dynamic attention mechanism to generate dynamic entity embeddings for different query relations. To ensure the semantic information of entities, the embedding-based and path-based learning are jointly trained for KGR. Additionally, the proposed dynamic aggregation mechanism operates on subgraphs, resulting in a remarkable 6–7 times resource conservation compared to GPU computations. Empirical experiments further demonstrate that DSA outperforms the current methods significantly on three benchmark datasets.

The Contrastive and Referential Function of Specific Classifiers in Xiamen Southern Min—Evidence from a Cognitive Experimental Study

Southern Min is generally known for not using classifiers [CL] for expressing definiteness/indefiniteness as it is associated with the bare classifier construction [CL N]. This paper offers evidence from Xiamen Southern Min (XSM) that the use of a specific classifier vs. the general classifier é contributes to referentiality in an alternative way by supporting object identification as it is due to the semantic specificity present in specific classifiers and absent in the general classifier. In a dialogic cognitive experiment adapted from the “Hidden color-chips” task (Enfield and Bohnemeyer 2001), 18 participants had to manipulate their addressees’ attention toward various objects situated in their immediate physical space through language as well as deictic gestures. The objects were associated with different specific classifiers or with the general classifier, and they were arranged according to the factors of (a) distance from speaker, (b) visibility for speaker, and (c) uniqueness (adjacency of similar items). The results show, among other things, that there is a higher tendency to use the specific CL in the [demonstrative CL N] construction if adjacent similar objects [−unique] are too far away from the speaker for clear identification by a demonstrative or a pointing gesture. This is seen as a last-resort strategy for creating contrast. Further corroboration comes from the use of specific classifiers in later mentions after the general CL failed to achieve clear identification. These findings can be situated in the broader context of other languages with classifiers in contrastive function (Thai, Vietnamese, and Ponapean) and they show the relevance of using dialogic texts for modeling classifier selection in contrast to narrative texts. Finally, dialogic contexts may serve as bridging contexts for grammaticalization from numeral classifiers to definiteness markers.

Maximality and modality in infinitival Wh-complements

Infinitival Wh-complements exhibit a modal interpretation, which arises despite there being no overt lexical modal. In the only comprehensive analysis of the modality, Bhatt (1999) argues that it should be attributed to a covert modal operator. The semantics of this operator is more specific than the typical Kratzerian one. This paper reexamines the original motivation underpinning Bhatt's analysis of the covert modal, and argues that such facts can be accounted for if the interrogative component of infinitival Wh-complements is specified to denote only maximal answers – a solution that is needed more generally in the interpretation of questions. Thus, it is not necessary to imbue the covert modal with a more specific semantics, making it possible to more broadly unify modal Wh-questions and infinitival Wh-complements.

Multi-Label Text Classification model integrating Label Attention and Historical Attention

Multi-Label Text Classification (MLTC) is one of the most import research of natural language processing. Although Deep Learning (DL) models have been widely applied to MLTC, there still exist some drawbacks. First, traditional DL models utilize all the words in the document to construct the embedding vector, while there are many words that affect the classification results. Next, the labels in MLTC have specific semantics, while traditional DL models ignore fine-grained matching signals between words and labels. Then, traditional DL models have difficulty in handling the data imbalance issue in MLTC datasets. In addition, during the training process, small errors in a certain epoch may be amplified with the increase of the number of iterations, resulting in classification errors. To address the above problems, a MLTC model integrating Label Attention and Historical Attention (i.e. LAHA) is proposed. First, a word filter is set up to select important words based on the cosine similarity between words and labels. Next, Document Self Attention (DSA) and Label Attention (LA) are obtained, and DSA-attended LA co-attention (LA-co) and LA-attended DSA co-attention (DAS-co) networks are constructed. Then, the fine-grained matching signals between words and labels are integrated through the adaptive plus of LA-co and DAS-co. At last, Historical Attention is integrated to LAHA, which not only avoids mis-classification caused by minor errors of a certain epoch, but also reduces overfitting to high-frequency labels. Multiple comparative experiments on four benchmark datasets demonstrate that LAHA outperforms the state-of-the-art baseline models and can effectively solve the data imbalance issue in MLTC datasets. Our code is available at https://github.com/sgysgywaityou/LAHA.

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

Genre specifics of the modern novel in the context of psychopoetics

ABSTRACT The goal of the article is to consider the specifics of the genre semantics of the modern novel in the context of the psychopoetics development. Alongside this, the objective of the work is to revisit the novels through the characteristics of their varieties and to try to answer the question – why the novels under consideration can be specified as psychological. A significant element of the article is devoted to the study of the representation techniques in modern Ukrainian novels in the context of psychopoetics. This research resulted in defining the genre semantics of the novels by the modern Ukrainian authors, ways of their implementation in the light of psychopoetics, psychonarratives, psychological types and means of psycho-poetic representation. The practical significance of the research is determined by the contribution to the development of psychopoetics as a branch of modern literary studies. The research outcomes can be used to design elective philological courses, comparative studies, and also for the advanced research on historical-cultural and psychological aspects of the essence of creativity in general.

Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection

Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facilitate the practical application of LD models, enabling fast speeds and limited memories without labeled data. However, prior PTQ methods do not consider the complex LD outputs that contain physical semantics, such as offsets, locations, etc., and thus cannot be directly applied to LD models. In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distortion Score. Moreover, we identify two main factors impacting the LD performance after quantization, namely intra-head sensitivity and inter-head sensitivity, where a small quantization error in specific semantics can cause significant lane distortion. Thus, we propose a Selective Focus framework deployed with Semantic Guided Focus and Sensitivity Aware Selection modules, to incorporate post-processing information into PTQ reconstruction. Based on the observed intra-head sensitivity, Semantic Guided Focus is introduced to prioritize foreground-related semantics using a practical proxy. For inter-head sensitivity, we present Sensitivity Aware Selection, efficiently recognizing influential prediction heads and refining the optimization objectives at runtime. Extensive experiments have been done on a wide variety of models including keypoint-, anchor-, curve-, and segmentation-based ones. Our method produces quantized models in minutes on a single GPU and can achieve 6.4\% F1 Score improvement on the CULane dataset. Code and supplementary statement can be found at https://github.com/PannenetsF/SelectiveFocus.

Respiration-based human-machine interface for aphasic patients with limited physical mobility

Communication between aphasic patients and health care professionals (HCPs) is often difficult, which is even more complex for aphasic patients with limited physical mobility. In this study, we have proposed a C60-OH/PDDA/SiO2-based humidity sensor with an extremely short response/recovery time (13 ms/30 ms). This is closely related to the electrostatic adsorption of PDDA which effectively alleviates the aggregation of C60-OH and the introduction of SiO2 microspheres which increases the diffusion space of water molecules. The fast response and recovery rates gave the sensor good respiratory monitoring capability, which can be used to develop a human–machine interface (HMI) to assist aphasic patients for semantic expression. In this HMI, aphasia patients can generate different respiratory signals through the ultrafast humidity sensors to express specific semantics (such as thirst, hunger, etc.). In contrast to traditional semantic expression devices, this interface does not require the user to have vocal or physical movement, and therefore is extremely friendly to aphasic patients with limited mobility.

Predictive Brain Activity Shows Congruent Semantic Specificity in Language Comprehension and Production.

Sentence fragments strongly predicting a specific subsequent meaningful word elicit larger preword slow waves, prediction potentials (PPs), than unpredictive contexts. To test the current predictive processing models, 128-channel EEG data were collected from both sexes to examine whether (1) different semantic PPs are elicited in language comprehension and production and (2) whether these PPs originate from the same specific "prediction area(s)" or rather from widely distributed category-specific neuronal circuits reflecting the meaning of the predicted item. Slow waves larger after predictable than unpredictable contexts were present both before subjects heard the sentence-final word in the comprehension experiment and before they pronounced the sentence-final word in the production experiment. Crucially, cortical sources underlying the semantic PP were distributed across several cortical areas and differed between the semantic categories of the expected words. In both production and comprehension, the anticipation of animal words was reflected by sources in posterior visual areas, whereas predictable tool words were preceded by sources in the frontocentral sensorimotor cortex. For both modalities, PP size increased with higher cloze probability, thus further confirming that it reflects semantic prediction, and with shorter latencies with which participants completed sentence fragments. These results sit well with theories viewing distributed semantic category-specific circuits as the mechanistic basis of semantic prediction in the two modalities.

Learnable convolutional attention network for knowledge graph completion

Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been used extensively in knowledge graph completion (KGC), which aims to solve the incompleteness of knowledge graphs (KGs). However, both GCNs and GATs have limitations in the KGC task, and the best method is analyzing the neighbors of each entity (pre-validating), while this process is prohibitively expensive. Furthermore, relations in KGs have specific semantics and should be considered when aggregating neighbor information (message passing). To address the above limitations, we propose a learnable convolutional attention network for knowledge graph completion named LCA-KGC. LCA-KGC introduces a knowledge graph convolutional attention network using a convolution operation before the attention mechanism to ensure structural information acquisition and avoid redundant information stacking. Furthermore, to complete the autonomous switching of GNNs types and eliminate the necessity of pre-validating the local structure of KGs, LCA-KGC designs a learnable knowledge graph convolutional attention network by comprising three types of GNNs in one learnable formulation. Moreover, a learnable message function is proposed to emphasize relational semantics when aggregating neighbor information. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and LCA-KGC achieves state-of-the-art (SOTA) performance compared to existing approaches (e.g., compared to SOTA approaches, LCA-KGC improves MRR from 0.360 to 0.372 on FB15k-237 dataset, and Hits@3 from 0.561 to 0.581 on YAGO3-10 dataset).

Synchronization-Inspired Interpretable Neural Networks.

Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. Inspired by biological brains and previous neuroscience studies, we propose an interpretable neural network incorporating a synchronization mechanism. The basic idea is to constrain each neuron, such as a convolution filter, to capture a single semantic pattern while synchronizing similar neurons to facilitate the formation of interpretable functional modules. Specifically, we regularize the activation map of a neuron to surround its focus position of the activated pattern in a sample. Moreover, neurons locally interact with each other, and similar ones are synchronized together during the training phase adaptively. Such local aggregation preserves the globally distributed representation nature of the neural network model, enabling a reasonably interpretable representation. To analyze the neuron interpretability comprehensively, we introduce a series of novel evaluation metrics from multiple aspects. Qualitative and quantitative experiments demonstrate that the proposed method outperforms many state-of-the-art algorithms in terms of interpretability. The resulting synchronized functional modules show module consistency across data and semantic specificity within modules.

Determining user specific semantics of locations extracted from trajectory data

Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level.

TRANSLATION ACTIVITY AND LINGUISTIC THINKING AS A SYSTEM OF HARMONIOUS METHODS FOR IDENTIFYING THE STRUCTURE AND THE CONTENT OF PSYCHO-LINGUISTIC ELEMENTS OF THE TEXT

The purpose of our research is to display translation activity and linguistic thinking as a system of harmonious methods for identifying the structure and the content of psycho-linguistic elements of the text.
 It was shown, that understanding of translation activity allows us to consider the issues, which were the most important for our research in a different way. What it is important is not the distinction of objects of the translation, but the formation of technology for the purpose of constructing translation objects (events, realities, and images), their semantic (semiotic) specification, the discovery of a universal translation algorithm, and the expansion of the methodological base for translation operations. The detection of such operations, their psycholinguistic analysis and structuring become quite important for the development of translation competence of the individual, which is one of the leading factors of active translation activity. In addition, it allows us to consider the process of translation at the level of diachrony and synchrony the text, as a process of generation, use and forgetting of translation techniques, bringing the translation activity closer to the interpretive theory of translation. Within the limits of the latter, it becomes possible for us to define the content of concepts, which are important for translation activity, such as a point of view, composition, a plot and a fable, connotative repertoire, strong positions (semantic milestones) and key words, meanings, senses and representations, referential and text situations, modal worlds of the author, reader’s and text’s expression from the point of view of their interconnection and interpenetration.
 We define linguistic thinking as a system of methods for purposefully identifying the structure and the content of linguistic elements, analyzing the peculiarities of their relationships, revealing the specifics of phonetic, lexical, grammatical and other phenomena and the laws of the language that are being learned. The formation of linguistic thinking ensures a high-quality solution to professional tasks and becomes a necessary condition for the person’s conscious and systematic mastery of a foreign language. So, from our point of view, linguistic thinking involves a thorough examination and study of the language as a system. As a result, this approach requires a detailed analysis of the structure of the language, its mechanisms and principles of the formation from the side of subjects of learning.

Heterogeneous Graph Neural Network With Multi-View Representation Learning

In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain node embeddings with versatility. To address the problem, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heterogeneous Graph Neural Network</i> for HG embedding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">within a Multi-View representation learning framework</i> (named MV-HetGNN), which consists of a view-specific ego graph encoder and auto multi-view fusion layer. MV-HetGNN thoroughly learns complex heterogeneity and semantics in the local structure to generate comprehensive and versatile node representations for HGs. Extensive experiments on three real-world HG datasets demonstrate the significant superiority of our proposed MV-HetGNN compared to the state-of-the-art baselines in various downstream tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> . node classification, node clustering, and link prediction.