Semantic Expression Research Articles

Exploring semantic expression disparities in intragenerational and intergenerational communication: A novel perspective on socioemotional selectivity theory.

The Socioemotional Selectivity Theory (SST) posits that older and younger adults have different life goals due to differences in perceived remaining lifetime. Younger adults focus more on future-oriented knowledge exploration and forming new friendships, while older adults prioritize present-focused emotional regulation and maintaining close relationships. While previous research has found these age differences manifest in autobiographical textual expressions, their presence in verbal communication remains unexplored. We recruited 36 older adults and 36 younger adults to form 12 younger adults-younger adults dyads, 12 older adults-older adults dyads, and 12 younger adults-older adults dyads engaging in three conversational scenarios. Analysis of communication transcripts using Linguistic Inquiry and Word Count revealed that in intragenerational communication, older adults used more present-focused and family-related words, and fewer future-focused, negative emotion, and knowledge acquisition words compared to younger adults, aligning with SST predictions. However, in intergenerational communication, younger and older adults showed similar patterns in future-focused, cognitive, and family-related language use. This suggests that participants adjusted their goal orientations to accommodate each other, mitigating the age differences proposed by SST and partially supporting the Interpersonal Cognitive Consistency Model. These findings indicate that while SST explains semantic expressions in intragenerational communication, group dynamics toward verbal consistency play a more vital role in intergenerational communication. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A discrete convolutional network for entity relation extraction.

Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations. In this paper, a discrete convolutional network is proposed to incorporate discrete linguistic interactions and deep feature weighting. This network applies a discretization strategy to fix parameters of convolutional kernels into ternary values. Then, these discretized kernels are used to learn discrete semantic structures from vectorized token representations. Our approach leverages the ability of discrete CNNs to capture discrete linguistic patterns of a sentence, thereby maintaining model expressiveness and improving performance in the relation extraction task. Furthermore, our method has the advantages of reducing the overfitting problem caused by depending on prior knowledge and decreasing the computational complexity by reducing the number of trainable parameters. Our model is evaluated on five widely used benchmark datasets. It achieves state-of-the-art performance, outperforming all compared related works. Experimental results also demonstrate that, compared with traditional CNN networks, it achieves an average improvement of 14.66% in F1-score and accelerates training by an average of 17.46%, highlighting the efficiency and effectiveness of our model in the relation extraction task.

О‘ZBEK VА INGLIZ MАQОLLАRIDА AYOL ROLINING IFОDАLАNISHI

This article compares Uzbek and English proverbs from a semantic and linguocultural perspective, analyzing the expression of gender semantics in them, especially the attitude towards women in society. Examples of proverbs expressing the female gender in the two languages ​​are given, and their similarities and differences are explained. Proverbs are analyzed based on the social status, marital status, intelligence, character, and moral standards of women in peoples belonging to different language families and different social societies.

Modeling the Urban Low-Altitude Traffic Space Based on the Land Administration Domain Model—Case Studies in Shenzhen, China

The urban low-altitude airspace is an integral part of urban space. As low-altitude utilization activities are being performed closer to the land surface, the management of the low-altitude space has become a focus of land administration. The management of the low-altitude airspace faces challenges such as cross-departmental coordination, fuzzy airspace boundaries, and complex spatial expressions. In 2020, the concept of “3D land administration” was introduced, marking the emergence of three-dimensional geospatial regulation in land management practices. Semantic models featuring the LADM (Land Administration Domain Model) as their core are updated iteratively to promote various applications related to 3D geographic regulation, but there is still a gap in the research on low-altitude utilization. Drawing upon Chinese regulations and policies, this paper employs the LADM framework to achieve semantic descriptions and expressions for managing areas in the low-altitude airspace: (1) relevant policies governing low-altitude spaces in China are analyzed, and the boundary between low-altitude and surface management is discussed; (2) the LADM structure is utilized to establish a comprehensive model for regulating low-altitude spaces; (3) and the capability of the LADM to support 3D low-altitude modeling is demonstrated through practical use cases in Shenzhen, China. Finally, the paper provides a comprehensive overview of the avenues for improvement and prospects.

Risk assessment of flammable liquid transportation on waterways: An ontology-driven dynamic Bayesian network approach

Accidents involving the transportation of flammable liquids on waterways often lead to severe consequences, highlighting the importance of effective risk assessment under conditions of uncertainty. This paper presents a novel methodology for evaluating the risk associated with the spatiotemporal evolution of flammable liquid transportation on waterways. By leveraging ontology models and dynamic Bayesian networks, the approach involves analyzing factors that impact risk, constructing a standardized knowledge representation model, and mapping this onto a dynamic Bayesian network for comprehensive risk assessment. The methodology incorporates fuzzy theory and the Best-Worst Method to calculate probabilities within the Bayesian framework, enabling detailed analysis of factor impacts on transportation risk. A practical application is illustrated through the development of a dynamic risk evaluation model for octane transportation in the Yangtze River, demonstrating the model's capability to predict risk under varying conditions. This ontology-driven Bayesian network model provides a robust foundation for making informed management decisions in the transportation of flammable liquids, effectively addressing the challenges of semantic expression and inferencing under uncertainty to enhance safety in waterway transportation.

Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining?

The field of multimedia research has witnessed significant interest in leveraging multimodal pretrained neural network models to perceive and represent the physical world. Among these models, vision-language pretraining (VLP) has emerged as a captivating topic. Currently, the prevalent approach in VLP involves supervising the training process with paired image-text data. However, limited efforts have been dedicated to exploring the extraction of essential linguistic knowledge, such as semantics and syntax, during VLP and understanding its impact on multimodal alignment. In response, our study aims to shed light on the influence of comprehensive linguistic knowledge encompassing semantic expression and syntactic structure on multimodal alignment. To achieve this, we introduce SNARE , a large-scale multimodal alignment probing benchmark designed specifically for the detection of vital linguistic components, including lexical, semantic, and syntax knowledge. SNARE offers four distinct tasks: Semantic Structure, Negation Logic, Attribute Ownership, and Relationship Composition. Leveraging SNARE , we conduct holistic analyses of six advanced VLP models (BLIP, CLIP, Flava, X-VLM, BLIP2, and GPT-4), along with human performance, revealing key characteristics of the VLP model: (i) Insensitivity to complex syntax structures, relying primarily on content words for sentence comprehension. (ii) Limited comprehension of sentence combinations and negations. (iii) Challenges in determining actions or spatial relations within visual information, as well as difficulties in verifying the correctness of ternary relationships. Based on these findings, we propose the following strategies to enhance multimodal alignment in VLP: (1) Utilize a large generative language model as the language backbone in VLP to facilitate the understanding of complex sentences. (2) Establish high-quality datasets that emphasize content words and employ simple syntax, such as short-distance semantic composition, to improve multimodal alignment. (3) Incorporate more fine-grained visual knowledge, such as spatial relationships, into pretraining objectives. 1

On the Semantic Expressiveness of Iso- and Equi-Recursive Types

Recursive types extend the simply-typed lambda calculus (STLC) with the additional expressive power to enable diverging computation and to encode recursive data-types (e.g., lists). Two formulations of recursive types exist: iso-recursive and equi-recursive. The relative advantages of iso- and equi-recursion are well-studied when it comes to their impact on type-inference. However, the relative semantic expressiveness of the two formulations remains unclear so far. This paper studies the semantic expressiveness of STLC with iso- and equi-recursive types, proving that these formulations are equally expressive. In fact, we prove that they are both as expressive as STLC with only term-level recursion. We phrase these equi-expressiveness results in terms of full abstraction of three canonical compilers between these three languages (STLC with iso-, with equi-recursive types and with term-level recursion). Our choice of languages allows us to study expressiveness when interacting over both a simply-typed and a recursively-typed interface. The three proofs all rely on a typed version of a proof technique called approximate backtranslation. Together, our results show that there is no difference in semantic expressiveness between STLCs with iso- and equi-recursive types. In this paper, we focus on a simply-typed setting but we believe our results scale to more powerful type systems like System F.

Semi-supervised spatial-temporal calibration and semantic refinement network for video polyp segmentation

Automated video polyp segmentation (VPS) was of vitality for the early prevention and diagnosis of colorectal cancer (CRC). However, existing deep learning-based automatic polyp segmentation methods mainly focused on independent static images and struggled to perform well due to neglecting spatial–temporal relationships among successive video frames, while requiring massive frame-by-frame annotations. To better alleviate these challenges, we proposed a novel semi-supervised spatial–temporal calibration and semantic refinement network (STCSR-Net) dedicated to VPS, which simultaneously considered both the inter-frame temporal consistency in video clips and intra-frame semantic-spatial information. It was composed of a segmentation pathway and a propagation pathway by use of a co-training scheme for supervising the predictions on un-annotated images in a semi-supervised learning fashion. Specifically, we proposed an adaptive sequence calibration (ASC) block in segmentation pathway and a dynamic transmission calibration (DTC) block in propagation pathway to fully take advantage of valuable temporal cues and maintain the prediction temporally consistent among consecutive frames. Meanwhile, in these two branches, we introduced residual block (RB) to suppress irrelevant noisy information and highlight rich local boundary details of polyp lesions, while constructed multi-scale context extraction (MCE) module to enhance multi-scale high-level semantic feature expression. On that basis, we designed progressive adaptive context fusion (PACF) module to gradually aggregate multi-level features under the guidance of reinforced high-level semantic information for eliminating semantic gaps among them and promoting the discrimination capacity of features for targeting polyp objects. Through synergistic combination of RB, MCE and PACF modules, semantic-spatial correlations on polyp lesions within each frame could be established. Coupled with the context-free loss, our model merged feature representations of neighboring frames to diminish the dependency on varying contexts within consecutive frames and strengthen its robustness. Extensive experiments substantiated that our model with 100% annotation ratio achieved state-of-the-art performance on challenging datasets. Even trained under 50% annotation ratio, our model exceled significantly existing state-of-the-art image-based and video-based polyp segmentation models on the newly-built local TRPolyp dataset by at least 1.3% and 0.9% enhancements in both mDice and mIoU, whilst exhibited comparable performance to top rivals attained through using fully supervised approach on publicly available CVC-612, CVC-300 and ASU-Mayo-Clinic benchmarks. Notably, our model showcased exceptionally well in videos containing complex scenarios like motion blur and occlusion. Beyond that, it also harvested approximately 0.794 mDice and 0.707 mIoU at an inference of 0.036 s per frame in endoscopist-machine competition, which outperformed junior and senior endoscopists as well as almost matched with those of expert ones. The strong capability of the proposed STCSR-Net held promise in improving quality of VPS, accentuating model’s adaptability and potential in real-world clinical scenarios.

A hierarchical sorting method based on hierarchical clustering and its application in port evaluation

Abstract A hierarchical sorting method with semantic description is developed to address sorting issues in management decisions. This approach achieves optimal hierarchical clustering results by incorporating indicators of intra-similarity, inter-similarity, and total similarity. Additionally, it integrates the axiomatic fuzzy sets theory to produce a semantic description of the hierarchical sorting results. The developed method can provide a good semantic expression for sorting results from a multi-level perspective and assist decision-makers in making reasonable decisions at different levels. This study takes the 40 main seaports as application examples for analyzing the applicability and practicability of the designed method.

EMFSA: Emoji-based multifeature fusion sentiment analysis

Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness.

Bistable perception of symbolic numbers.

Numerals, that is, semantic expressions of numbers, enable us to have an exact representation of the amount of things. Visual processing of numerals plays an indispensable role in the recognition and interpretation of numbers. Here, we investigate how visual information from numerals is processed to achieve semantic understanding. We first found that partial occlusion of some digital numerals introduces bistable interpretations. Next, by using the visual adaptation method, we investigated the origin of this bistability in human participants. We showed that adaptation to digital and normal Arabic numerals, as well as homologous shapes, but not Chinese numerals, biases the interpretation of a partially occluded digital numeral. We suggest that this bistable interpretation is driven by intermediate shape processing stages of vision, that is, by features more complex than local visual orientations, but more basic than the abstract concepts of numerals.

Construction and Inference Method of Semantic-Driven, Spatio-Temporal Derivation Relationship Network for Place Names

As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently explored the spatio-temporal derivation relationships of place names, failing to fully utilize these relationships to enhance the connectivity between place names and improve spatial retrieval capabilities. Therefore, this paper conducts research on the spatio-temporal derivation relationships of place names, defines them in a standardized manner, clarifies the boundary conditions and identification methods, and then constructs a spatio-temporal derivation network of place names for expression and uses this network to carry out reasoning research on spatial adjacency relationships. Experiments and results showed that using the theory and methods of this paper to identify the spatio-temporal derivation relationships of Canadian place names achieves an accuracy rate of 98.5% and a recall rate of 93.4%, and the reasoning results can effectively improve the accuracy of query results. The research enriches the theoretical framework of spatio-temporal derivation relationships of place names, solves the current problems of unclear definition and inability to automatically identify spatio-temporal derivation relationships, and provides new perspectives and tools for the application practice in the field of geographical information science.

Social Network Public Opinion Analysis Using BERT-BMA in Big Data Environment

The existing social network public opinion analysis methods have problems such as poor semantic expression quality and weak detection ability in short texts. Therefore, a social network public opinion analysis method based on BERT-BMA is proposed. To normalize the comment text, the rumor text is initially transferred to a word vector matrix using the BERT (Bidirectional Encoder Representations from Transformer) model. The BiLSTM-based network architecture is subsequently employed to acquire the trace features of data transmission. Ultimately, this study employs the multi-head attention mechanism to extract feature information that is more significant in the analysis of online public opinion by mining the dependency relationships between users, resulting in increasing ability to detect public opinion emergencies. The experimental outcomes indicate that the results on the Twitter data set and Weibo dataset are superior to other comparative models.

Frame-level nonverbal feature enhancement based sentiment analysis

Multimodal Sentiment Analysis (MSA) comprehensively utilizing data from multiple modalities to obtain more accurate sentiment attribute, has important applications in other fields, such as social media analysis, user experience evaluation and medical health, etc. It is worth noting that previous studies have paid little attention to the inconsistency of the initial representation granularity between verbal (textual) and nonverbal (acoustic and visual) modalities. As a result, the imbalanced emotional information between them complicates the interaction process, and ultimately affects the model’s performance. To solve this problem, this paper proposes a Frame-level Nonverbal feature Enhancement Network (FNENet) to improve performance on MSA by reducing the gap and integrating asynchronous affective information between modalities. Specifically, Vector Quantization (VQ) is applied to nonverbal modalities to reduce the granularity differences and improve the performance of the model. Additionally, nonverbal information is integrated through the Sequence Fusion mechanism (SF) into a pre-trained language model to enhance the textual representation, which benefits the word-level semantic expression according to the asynchronous affective cues preserved in unaligned frame-level nonverbal features. Extensive experiments on three benchmark datasets demonstrate that FNENet significantly outperforms baseline methods. It indicates that our model has potential application on MSA.

Sparse convolutional model with semantic expression for waste electrical appliances recognition

Sparse convolutional model with semantic expression for waste electrical appliances recognition

HIST: Hierarchical and sequential transformer for image captioning

AbstractImage captioning aims to automatically generate a natural language description of a given image, and most state‐of‐the‐art models have adopted an encoder–decoder transformer framework. Such transformer structures, however, show two main limitations in the task of image captioning. Firstly, the traditional transformer obtains high‐level fusion features to decode while ignoring other‐level features, resulting in losses of image content. Secondly, the transformer is weak in modelling the natural order characteristics of language. To address theseissues, the authors propose a HIerarchical and Sequential Transformer (HIST) structure, which forces each layer of the encoder and decoder to focus on features of different granularities, and strengthen the sequentially semantic information. Specifically, to capture the details of different levels of features in the image, the authors combine the visual features of multiple regions and divide them into multiple levels differently. In addition, to enhance the sequential information, the sequential enhancement module in each decoder layer block extracts different levels of features for sequentially semantic extraction and expression. Extensive experiments on the public datasets MS‐COCO and Flickr30k have demonstrated the effectiveness of our proposed method, and show that the authors’ method outperforms most of previous state of the arts.

Toward a historical grammar of elevation in Kartvelian

This paper in detail addresses and, for the first time in the field, presents linguistic evidence related to the expression of elevational semantics in the Kartvelian languages. The domains of its expression include a wide range of parts of speech, such as adverbs, adjectives, postpositions, and verbs. Provided with a broad synchronic illustrative base, as well as with respective diachronic commentaries, the material under discussion is meant to lay the groundwork for the establishment of a diachronic grammar of elevation in Kartvelian.

Surface Defect Detection for Aerospace Aluminum Profiles with Attention Mechanism and Multi-Scale Features

A YOLOv5 aluminum profile defect detection algorithm that integrates attention and multi-scale features is proposed in this paper to address the issues of the low detection accuracy, high false detection rates, and high missed detection rates that are caused by the large-scale variation of surface defects, inconspicuous small defect characteristics, and a lack of concentrated feature information in defect areas. Firstly, an improved CBAM (Channel-Wise Attention Module) convolutional attention module is employed, which effectively focuses on the feature information of defect areas in the aluminum defect dataset with only a small amount of spatial dimension. Secondly, a bidirectional weighted feature fusion network is utilized, incorporating a multi-scale feature fusion network with skip connections to aggregate various high-resolution features, thus enriching the semantic expression of features. Then, new size feature maps that have not been fused are introduced into the detection layer network to improve the detection effect of small target defects. Experimental results indicate that an average detection accuracy (mAP) of 82.6% was achieved by the improved YOLOv5 algorithm on the aluminum surface defect dataset. An improvement of 6.2% over the previous version was observed. The current defect detection requirements of aluminum profile production sites are met by this enhanced algorithm.

A rule reasoning diagram for visual representation and evaluation of belief rule-based systems

Belief rule-based (BRB) systems are usually regarded as much more interpretable than those systems based on black-box techniques. Unfortunately, understanding the reasoning behavior of BRB systems is a complex task, although the use of linguistic variables and rules makes BRB systems have outstanding semantic expressiveness. In this study, a rule reasoning diagram, named RULING, is proposed as a tool for visual representation and exploratory analysis of the BRB systems. RULING describes the BRB system in the form of a network graph based on the interaction between the rules, which allows users to understand the behavior of the BRB system by examining the information represented graphically, and provides an effective means to improve the interpretability of BRB systems. In the meanwhile, some quantitative indicators based on the network structure and rule characteristics are proposed to evaluate the performance of rules and BRB systems. A classification case and a regression case are studied as examples to illustrate the feasibility and effectiveness of RULING. The results indicate that RULING can offer many possibilities for BRB system analysis and evaluation, such as identifying important rules, interpreting rule interactions, detecting inconsistent rules, and guiding the adjustment of the rule base.

Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation

Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that aggregate explicit social links have shown promising prospects, they are often constrained by the absence of explicit social data and the assumption of homogeneity, thus overlooking variations in social influence and consistency. These limitations hinder semantic expression and recommendation performance. Therefore, we propose a novel framework for social recommendation. First, we design a bipartite network embedding scheme, which learns vertex representations in the embedding space by modeling 1st-order explicit relations and higher-order implicit relations between vertices. Then, the similarity of the embedding vectors is used to extract top-k semantically consistent friends for each user. Next, we design an algorithm to assign a specific influence value to each user. Finally, we combine the top-k friends of the user and their influence values into an ensemble and add it as a regularization term to the rating prediction process of the user to correct the bias. Experiments on three real benchmark datasets show significant improvements in EISF over state-of-the-art methods.