Semantic Change Research Articles

The Linguistic Legacy of Organized Crime in the 1990s in Post-Communist Countries

After the fall of communist regimes, post-communist societies experienced a period of intense political change. New political parties and structures emerged that created both new programs and concepts as well as terminology. The vocabulary of any language is dynamic and constantly evolving in line with changes in society. As for the language of the 1990s in post-communist societies, we can point out that the language is a mirror of the life around us and its economic, political and social upheavals. The new realities are reflected in the lexical system of the language with a special evaluation embedded in the semantics or connotation of the described vocabulary. Those words are actually key concepts of the "new life" and everyday life of the transition and can become guidelines in the study of this modern period. The text presents part of the language corpus created by us of the words from the 1990s. The object of our research are the lexemes related to organized crime. Vocabulary related to that semantic field contains a large amount of expressiveness or change in the original semantics.

An embedded diachronic sense change model with a case study from ancient Greek

Word meanings change over time, and word senses evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as “kosmos” (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. This paper introduces EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. It is shown empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. The challenges of fitting these models are also discussed.

Weight Saliency search with Semantic Constraint for Neural Machine Translation attacks

Text adversarial attack is an effective way to improve the robustness of Neural Machine Translation (NMT) models. Existing NMT attack tasks are often completed by replacing words. However, most of previous works pursue a high attack success rate but produce semantic inconsistency sentences, leading to wrong translations even for humans. In this paper, we propose a Weight Saliency search with Semantic Constraint (WSSC) algorithm to make semantic consistency word modifications to the input sentence for black-box NMT attacks. Specifically, our WSSC has two major merits. First, it optimizes the word substitution with a word saliency method, which is helpful to reduce word replacement rate. Second, it constrains the objective function with a semantic similarity loss, ensuring every modification does not lead to significant semantic changes. We evaluate the effectiveness of the proposed WSSC by attacking three popular NMT models, i.e., T5, Marian, and BART, on three widely used datasets, i.e., WMT14, WMT16, and TED. Experimental results validate that our WSSC improves Attack Success Rate (ASR) by 4.02% and Semantic Similarity score (USE) by 1.28% on average. Besides, our WSSC also shows good properties in keeping grammar correctness and transfer attack.

ترجمة النوع (الجنس) في تعليمات ملصقات المنتجات من اللغة الإنجليزية إلى اللغة العربية

The study at hand aimed at investigating the techniques that are adopted by translators when they encounter problems in translating gender for the directions of products’ labels from a less detailed language (English) into a higher detailed language(Arabic).The current study also strived to highlight how much the stereotype of the translator and society culture can affect the selection of a translator in choosing the grammatical gender form to detect the source language ambiguity in determining the target gender consumer. To achieve these objectives, this study depended on the descriptive analytical approach where the researchers worked on describing and analyzing the Arabic target texts (TTs) that found on some selected labels which have been gathered by the researchers. The study concluded that adding one morpheme to the target text (TT) is not just a structural change necessity but also a semantic change necessity. The study recommended that translators should be aware of linguistic, cultural and social differences between Arabic and English. Besides, they should be familiar with translating signs and images which are available on products’ labels.

ReenRepair: Automatic and semantic equivalent repair of reentrancy in smart contracts

Reentrancy, the most notorious vulnerability in smart contracts, has attracted extensive attention. To eliminate reentrancy before deploying contracts, there is a need to locate and repair the contracts. However, existing tools suffer from false positive localization, original semantics destruction, and high gas overhead. In this work, we propose a template-based gas-optimized reentrancy repair method with semantic maintenance. We avoid false positive locations from verifying the attack’s effectiveness, using connectivity and read–write dependencies. We design the semantic equivalence check algorithm based on the def-use chain. We optimize the lock and reordering templates for reentrancy repair and add semantic maintenance operations. We implement our tool, ReenRepair, and compare it with two state-of-the-art detection tools and two repair tools. The results show that ReenRepair yields good location precision, the highest repair rate, and the lowest gas overhead. All semantic changes caused by lock and 89.66% of semantic changes caused by reordering are successfully maintained.

Hermeneutical disarmament

Abstract When words and phrases change their meaning, we might find ourselves less able to understand and communicate, and this can be harmful to us. I make sense of this by introducing the concept of hermeneutical disarmament. Hermeneutical disarmament is the process by which a person is rendered less able to understand or communicate experiences, ideas, and other phenomena as a result of semantic change to the linguistic resources that could previously have been deployed for these purposes. I defend this concept by showing that semantic change can inflict cognitive and communicative harms and detail some of the forms that it can take.

Building Damage Degree Recognition Based on Temporal Attention Features

Natural disasters pose significant harm to society. As an important place for social activities and economic development, the degree of damage to building areas is directly related to disaster loss assessment and emergency rescue. Remote sensing image data, characterized by its wide coverage and multi-temporal features, provides important data support for post-disaster loss assessment. However, imaging differences caused by factors such as shooting time, imaging angle, and different sensors can interfere with the extraction of damage features and loss assessment. This paper proposes a Dual-Exchange-Attention U-Net (DERU-Net) model, which transforms the identification of building damage levels into intra-class semantic change detection. The DFMA feature attention fusion module is introduced to enhance the ability of dual-temporal feature extraction and achieve end-to-end assessment of building damage. The proposed method is comprehensively evaluated and tested on the xBD dataset. Experimental results show that compared with other methods, the DERU-Net proposed in this paper exhibits better stability and evaluation accuracy in assessing the degree of building damage.

Turning night into day: Milieu and semantic change in Albanian

We bring to light here two case studies from Albanian that show interesting semantic change, and we argue that in order to understand the particular changes involved, the concept of “milieu” (Christiansen and Joseph 2016: 56-7) should be invoked.

Diachronic Semantic Tracking for Chinese Words and Morphemes over Centuries

Lexical semantic changes spanning centuries can reveal the complicated developing process of language and social culture. In recent years, natural language processing (NLP) methods have been applied in this field to provide insight into the diachronic frequency change for word senses from large-scale historical corpus, for instance, analyzing which senses appear, increase, or decrease at which times. However, there is still a lack of Chinese diachronic corpus and dataset in this field to support supervised learning and text mining, and at the method level, few existing works analyze the Chinese semantic changes at the level of morpheme. This paper constructs a diachronic Chinese dataset for semantic tracking applications spanning 3000 years and extends the existing framework to the level of Chinese characters and morphemes, which contains four main steps of contextual sense representation, sense identification, morpheme sense mining, and diachronic semantic change representation. The experiment shows the effectiveness of our method in each step. Finally, in an interesting statistic, we discover the strong positive correlation of frequency and changing trend between monosyllabic word sense and the corresponding morpheme.

Analysing lexical semantic changes in chinese educational texts by integrating machine learning methods

Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context.

Emerging Problems in Language and Translation, and Related Solutions During Epidemics

Healthcare, societal structures, the economy, education, and communication have been deeply affected by the COVID-19 pandemic. However, its impact extends to the various language aspects. This study aims to explore how the pandemic has influenced language use in communication, education, and technology, especially in Arabic language. The research investigates the semantic changes that occur in certain expressions due to the pandemic. Moreover, this study examines how translators navigate challenges when translating new English terms and expressions into Arabic using the Skopos Theory. Utilizing a qualitative approach, data were collected from various social and mass media sources. In conclusion, the research findings suggest the creation of new vocabulary and semantic shifts. Additionally, Arabic, like all languages, evolves in translation, leading translators to use various methods for accurate rendering of terms and expressions.

A disparity‐aware Siamese network for building change detection in bi‐temporal remote sensing images

AbstractBuilding change detection has various applications, such as urban management and disaster assessment. Along with the exponential growth of remote sensing data and computing power, an increasing number of deep‐learning‐based remote sensing building change detection methods have been proposed in recent years. Objectively, the overwhelming majority of existing methods can perfectly deal with the change detection of low‐rise buildings. By contrast, high‐rise buildings often present a large disparity in multitemporal high‐resolution remote sensing images, which degrades the performance of existing methods dramatically. To alleviate this problem, we propose a disparity‐aware Siamese network for detecting building changes in bi‐temporal high‐resolution remote sensing images. The proposed network utilises a cycle‐alignment module to address the disparity problem at both the image and feature levels. A multi‐task learning framework with joint semantic segmentation and change detection loss is used to train the entire deep network, including the cycle‐alignment module in an end‐to‐end manner. Extensive experiments on three publicly open building change detection datasets demonstrate that our method achieves significant improvements on datasets with severe building disparity and state‐of‐the‐art performance on datasets with minimal building disparity simultaneously.

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Water, indispensable for life and central to ecosystems, human activities, and climate dynamics, requires rapid and accurate monitoring. This is vital for sustaining ecosystems, enhancing human welfare, and effectively managing land, water, and biodiversity on both the local and global level. In the rapidly evolving domain of remote sensing and deep learning, this study focuses on water body extraction and classification through the use of recent deep learning models of visual foundation models (VFMs). Specifically, the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) models have shown promise in semantic segmentation, dataset creation, change detection, and instance segmentation tasks. A novel two-step approach involving segmenting images via the Automatic Mask Generator method of the SAM and the zero-shot classification of segments using CLIP is proposed, and its effectiveness is tested on water body extraction problems. The proposed methodology was applied to both remote sensing imagery acquired from LANDSAT 8 OLI and very high-resolution aerial imagery. Results revealed that the proposed methodology accurately delineated water bodies across complex environmental conditions, achieving a mean intersection over union (IoU) of 94.41% and an F1 score of 96.97% for satellite imagery. Similarly, for the aerial imagery dataset, the proposed methodology achieved a mean IoU of 90.83% and an F1 score exceeding 94.56%. The high accuracy achieved in selecting segments predominantly classified as water highlights the effectiveness of the proposed model in intricate environmental image analysis.

Semantic word change as the main way to create a comic effect in the speech genres of Internet communication

The subject of the study is the semantic change of a word as a way to create a comic effect in the Internet communication. The object of the research in the article is Internet memes. Special attention is paid in the article to new ways of creating a comic effect, which the authors of memes find against the background of an increase in the level of intra-genre competition. Comic genres of Internet communication are becoming a kind of thesaurus reflecting the current state of the Russian language and the changes that have occurred in it. Nevertheless, the novelty and dynamic change of linguistic trends in the speech comic genres of Internet communication suggest an insufficient theoretical justification of the problem, which determines the relevance of this work. To establish the methods used to create a comic effect, methods of analyzing dictionary definitions and contextual analysis of lexical units in the meme (including the influence of the non-verbal component) were applied. The main conclusions of the study: 1) there are a number of ways to create a comic effect based on a semantic change of a word; 2) certain features (conditions) of using these methods increase their effectiveness: the ambiguity of lexemes, the assignment of an essential role to the non-verbal component of a meme, the colloquial or slang character of the actualized meaning, the presence of a token, the recognition of verbal and non-verbal components, intentional violation of spelling rules. A special contribution of the authors and the novelty of the research is the study of ways to create a comic effect (based on semantic word change) in the genre of an Internet meme, including a non-verbal component that has a special effect on the recipient's perception of information.

Verbal vocabulary as a means of expressing interpersonal relations in Russian and Chinese

The subject of the study is the semantic and functional features of the lexico-semantic group of verbs denoting interpersonal relations in the Russian and Chinese languages. The verbal vocabulary representing the category of relationship describes the relationships that arise between people in the form of feelings, judgments and appeals to each other in the course of everyday life. The purpose of the proposed article is to identify the semantic and functional features of the verbs of the lexico-semantic group of interpersonal relations in the system of Russian and Chinese languages. The study revealed similarities and differences in the meaning of these verbs. As the analysis showed, some Chinese verbs do not match the meaning of the verbs of the Russian language. This article also examines the types of semantic changes in the semantic structure of the verbal word form functioning in the context. The main method of studying this group of verbs in Russian and Chinese is the comparative method. The descriptive method and the functional-semantic method, as well as the method of component analysis, were used in the selection and use of linguistic material. The scientific novelty of the study lies in the fact that the verbs of interpersonal relations are still insufficiently studied on the materials of the Russian and Chinese languages. The results of the research can be used in the practice of teaching university courses in such disciplines as cognitive linguistics, linguoculturology, and intercultural communication. The study allows us to conclude that the verbs of interpersonal relationships are quite often used in both languages. The meanings of Chinese and Russian verbs are not always equivalent. Their semantic structures may also differ. The analysis of interlanguage gaps reveals the presence of incomplete lexical correspondence to a foreign word. Some verbal lexicons express interpersonal relationships in their direct meanings, while others develop this ability as a result of semantic modulation and semantic derivation. Interpersonal relationships can be expressed by verbs from other lexico-semantic groups, acquiring the semantics of interpersonal relationships in the context. In conclusion, we note that expressions of interpersonal relationships can also be verb-noun combinations, which, when analyzing various specifiers in the context, indicate the reasons for a positive or negative attitude towards a person.

Образ мира сквозь призму оценочной лексики: трендовое исследование

The article describes the subjective semantics of the associative fields of two axiological stimuli and their role in the formation of the image of the world of Russian speakers of different ages in the trend mode. The associative fields “good” and “bad” are explored in the aspect of sociocultural changes in the axiological part of the image of the world among students of four generations. The research material was taken from the corresponding associative fields of the Dictionary of Associative Norms of the Russian Language ed. by A.A. Leontyev 1977), Russian Associative Dictionary ed. by Yu.N. Karaulov (2002), Russian regional associative base (Siberia and the Far East) ed. by I.V. Shaposhnikova 2008–2023), as well as the results of free associative experiments conducted by the authors in 2023. The purpose of the article is to analyze the associative fields of the stimuli good and bad in the indicated dictionaries and in the free associative experiment (2023); to identify common and different perceptions of these stimuli by native speakers of the Russian language from the point of view of time (the line of generational changes); determine the features of the subjective semantics of the vocabulary under study taking into account stable / dynamic components and the specifics of reflecting changes in the image of the world (the general line of trend research in semantics). The use of a trend approach to establish zones of constant response and possible reasons for changes in the subjective semantics of stimuli made it possible to consider the “associative profile” of images of consciousness that is “specific” to a given culture and language (N.V. Ufimtseva), to identify the sociocultural differences of respondents that influenced their image of the world, connect with changes in life and the historical situation in Russia, the expansion of civil liberties and freedom of speech, the transformation of interests, value guidelines, attitudes towards work and education, the active introduction of foreign cultural values, the spread of borrowed and slang vocabulary. It was found that some of these differences can be traced in dynamics from generation to generation and show in which direction the axiological part of the image of the world is changing.

Semantic change and word formation during Covid-19 in the Philippines

This study explored the semantic change and word formation of the English language terms used during the Covid-19 pandemic in the Philippines. There were 50 words selected from the memorandum and executive orders of the national and local government units and those from news reports posted online on social media (Facebook, Twitter, and Instagram). These words were analyzed using the components of the neologism framework proposed by Krishnamurthy (2010) on the word formation, borrowing, and lexical deviation and that of Crowley and Bowern (2010) for semantic change. The results of the study revealed four types of semantic change (i.e., widening, narrowing, semantic shift, and bifurcation), and the newly discovered words were categorized into compounding, acronym, and conversion. This study provides implications that in times of crisis (such as the Covid-19 pandemic), Filipinos tend to create and recreate words within a context easily understood by the community to establish clear communication.

Anomalous diffusion analysis of semantic evolution in major Indo-European languages.

How do words change their meaning? Although semantic evolution is driven by a variety of distinct factors, including linguistic, societal, and technological ones, we find that there is one law that holds universally across five major Indo-European languages: that semantic evolution is subdiffusive. Using an automated pipeline of diachronic distributional semantic embedding that controls for underlying symmetries, we show that words follow stochastic trajectories in meaning space with an anomalous diffusion exponent α = 0.45 ± 0.05 across languages, in contrast with diffusing particles that follow α = 1. Randomization methods indicate that preserving temporal correlations in semantic change directions is necessary to recover strongly subdiffusive behavior; however, correlations in change sizes play an important role too. We furthermore show that strong subdiffusion is a robust phenomenon under a wide variety of choices in data analysis and interpretation, such as the choice of fitting an ensemble average of displacements or averaging best-fit exponents of individual word trajectories.

Transformer-based multimodal change detection with multitask consistency constraints

Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging pre-event digital surface model (DSM) data and post-event digital aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. It adopts a consistency constraint to establish the multimodal relationship. Initially, pseudo-changes are derived by employing height change thresholding. Subsequently, the L2 distance between semantic and pseudo-changes within their overlapping regions is minimized. This explicitly endows the height change detection (regression task) and semantic change detection (classification task) with representation consistency. A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements.

Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA

Natural language explanation in visual question answer (VQA-NLE) aims to explain the decision-making process of models by generating natural language sentences to increase users' trust in the black-box systems. Existing post-hoc methods have achieved significant progress in obtaining a plausible explanation. However, such post-hoc explanations are not always aligned with human logical inference, suffering from the issues on: 1) Deductive unsatisfiability, the generated explanations do not logically lead to the answer; 2) Factual inconsistency, the model falsifies its counterfactual explanation for answers without considering the facts in images; and 3) Semantic perturbation insensitivity, the model can not recognize the semantic changes caused by small perturbations. These problems reduce the faithfulness of explanations generated by models. To address the above issues, we propose a novel self-supervised Multi-level Contrastive Learning based natural language Explanation model (MCLE) for VQA with semantic-level, image-level, and instance-level factual and counterfactual samples. MCLE extracts discriminative features and aligns the feature spaces from explanations with visual question and answer to generate more consistent explanations. We conduct extensive experiments, ablation analysis, and case study to demonstrate the effectiveness of our method on two VQA-NLE benchmarks.