Semantic Meaning Research Articles

Toward a framework of extracting typical machining process routines based on knowledge representation learning

With the application of CAPP systems, manufacturing enterprises have accumulated a large volume of process data of parts, which are important strategic resources. Deeply mining the value hidden in these process data to improve the reuse rate of process knowledge is the key to realize the intellectualization of process planning. This paper builds a novel framework of extracting typical machining process routines (TMPRs) based on knowledge representation learning. First, two graph models, which describe machining processes including the relationships among “part-material-process-machine tool-cutting tool”, are established to present the semantic meaning of process data through the connectivity of graph structures in a direct way. One model conforms to the standard process routes, while the other makes some modifications to make the node “parts” have more in-links and out-links. Then, TransD algorithm is used to map the entities and relations based on the two models into dense low-dimensional real-valued spaces, and the better model is determined. After that, the vectors representing the parts of the better model are clustered, and the results show that the parts with a similar operation sequence and processed by the same cutting tools and machine tools can be clustered into the same cluster. This method breaks through the singleness and limitation of the traditional classification depending on the features of parts. Furthermore, TMPRs are extracted from the cluster results to verify the expression ability of semantic meaning of these vectors. Finally, we package the main functions of the research results into corresponding software modules. Compared with the existing process reuse methods, the proposed framework is much more efficient and extensible.

A DEIXIS ANALYSIS OF SONG LYRICS IN NIKI’S “NICOLE” ALBUM

This study aimed to identify the kinds of Deixis and their influence on the formation of meaning in song lyrics. The object of the study was the entirety of the songs under Niki's album, namely, the "Nicole" album. The study examined 12 songs in total. The researchers used a qualitative-descriptive method to do the study as it was considered the best method to describe the analysis result. The result of the study stated that the three kinds of Deixis existed in semantics. There are 99 personal deixes, 15 spatial deixes, and 52 temporal deixis, with 166 deixes. It was identified that the song lyrics from the album mainly used personal. According to the study, Deixis plays an essential role in clarifying the meaning of song lyrics. This study not only offered supplementary information but also provided practical help for readers to comprehend better Deixis, including its various kinds, semantic meanings, and how the use of Deixis was able to influence song lyrics.

Two-Stream Network for Korean Natural Language Understanding

This study pioneers a dual-stream network architecture tailored for Korean Natural Language Understanding (NLU), focusing on enhancing comprehension by distinct processing of syntactic and semantic aspects. The hypothesis is that this bifurcation can lead to a more nuanced and accurate understanding of the Korean language, which often presents unique syntactic and semantic challenges not fully addressed by generalized models. The validation of this novel architecture employs the Korean Natural Language Inference (koNLI) and Korean Semantic Textual Similarity (koSTS) datasets. By evaluating the model's performance on these datasets, the study aims to determine its efficacy in accurately parsing and interpreting Korean text's syntactic structure and semantic meaning. Preliminary results from this research are promising. They indicate that the dual-stream approach significantly enhances the model's capability to understand and interpret complex Korean sentences. This improvement is crucial in NLU, especially for language-specific applications. The implications of this study are far-reaching. The methodology and findings could pave the way for more sophisticated NLU applications tailored to the Korean language, such as advanced sentiment analysis, nuanced text summarization, and more effective conversational AI systems. Moreover, this research contributes significantly to the broader field of NLU by underscoring the importance and efficacy of developing language-specific models, moving beyond the one-size-fits-all approach of general language models. Thus, this study is a testament to the potential of specialized approaches in language understanding technologies.

Graph Convolutional Network with Syntactic Dependency for Aspect-Based Sentiment Analysis

AbstractAspect-based sentiment analysis (ABSA) aims to mine the sentiment tendencies expressed by specific aspect terms. The studies of ABSA mainly focus on the attention-based approaches and the graph neural network approaches based on dependency trees. However, the attention-based methods usually face difficulties in capturing long-distance syntactic dependencies. Additionally, existing approaches using graph neural networks have not made sufficient exploit the syntactic dependencies among aspects and opinions. In this paper, we propose a novel Syntactic Dependency Graph Convolutional Network (SD-GCN) model for ABSA. We employ the Biaffine Attention to model the sentence syntactic dependencies and build syntactic dependency graphs from aspects and emotional words. This allows our SD-GCN to learn both the semantic relationships of aspects and the overall semantic meaning. According to these graphs, the long-distance syntactic dependency relationships are captured by GCNs, which facilitates SD-GCN to capture the syntactic dependencies between aspects and viewpoints more comprehensively, and consequently yields enhanced aspect features. We conduct extensive experiments on four aspect-level sentiment datasets. The experimental results show that our SD-GCN outperforms other methodologies. Moreover, ablation experiments and visualization of attention further substantiate the effectiveness of SD-GCN.

National and Cultural Content of the Heart Concept in the Axiological Component of the Worldview

The focus of the article is the universal Heart concept, which actively functions in many languages and is an integral component in constructing a figurative picture of the world. In the work, through the study of the Heart concept in the discourse of Russian, Kyrgyz, Dungan and Arabic languages, various semantic meanings and images evoked by it in the minds of speakers of different cultures are revealed; the national and cultural content of the concept is described; the features of the interpretation of the concept under consideration and its axiology are determined; the highest priority semantic meanings for the compared cultures are identified.

Signalling digression in Spanish and English: Evidence from parliamentary discourse

Digression is a discourse function that is identifiable and distinct from other similar functional strategies, and varies cross-linguistically. This paper investigates the way speakers of Spanish and English digress from their main discourse topics and how these transitions are marked. Once digression is defined by the proposal of four features considered to be inherent in this concept, the paper focuses on so-called 'digression formulating phrases', recurrent expressions which make explicit reference to the digression and signal that a new direction with respect to topic is proposed. The paper thus adopts a less common than the usual approach to discourse coherence and digression, whose literature has largely focused on discourse markers. The dataset, extracted from a corpus of parliamentary discourse, is analysed in an attempt to address two research questions. I investigate, first, the frequency and distribution of the selected digression formulating phrases in the two languages, and discern which of the two contexts of digression (i.e. beginnings and ends) is more highly marked, and, second, their use in connection to aspects such as syntactic configuration and position, and co-occurring features of semantic and pragmatic meaning. The contrastive perspective adds precision and richness to the treatment of digression.

Norms, convergent validity, test–retest reliability, and practice effects for verbal fluency overall performance, clustering, and switching in Spanish-speaking children

Objective We conducted two empirical studies (in a cross-sectional and a longitudinal design) with the aim at establishing normative data (including norms for strategy use [i.e., clustering and switching strategies] and performance over time), and examining the convergent validity, the test–retest reliability (3–4 wks interval) and the changes in performance with practice (1 year interval) of the different verbal fluency (VF) quantitative and qualitative scores in Spanish-speaking children and adolescents. Method In S1 (n = 620 6- to 15-year-old Spanish-speaking children and adolescents), MANCOVA and Pearson’s correlations were employed. In S2 (n = 148 6- to 12-year-old Spanish-speaking children), intraclass correlation coefficient (ICC), paired t-tests, and Confirmatory Factor Analysis (CFA) were used. Results S1 results showed an age effect on all VF measures (quantitative and qualitative). The number of switches/clusters was more related to total word productivity and to executive functions (EF) than the mean cluster size. In S2, a significant increase in phonological VF performance was observed on number of switches and word productivity scores from baseline (Time 1) to repeat testing at Time 2. Practice effects were observed at Time 3 on all measures except for semantic and phonological mean cluster size. Test–retest reliability coefficients at Time 2 for number of clusters and switches, but not for mean cluster size, fell in the moderate range, ranging from ICCs .61 to ICCs .81. Test–retest reliability coefficients for total word productivity were higher (ICCs above .80) and stronger when testing as a unity with CFA methods (ϕ=.94, p < .001). Conclusions These data may be relevant for informing the neuropsychological assessment of spontaneous cognitive flexibility in children with typical development (TD) and those with developmental or acquired disorders.

Adaptive Semantic-Enhanced Transformer for Image Captioning.

In the research on image captioning, rich semantic information is very important for generating critical caption words as guiding information. However, semantic information from offline object detectors involves many semantic objects that do not appear in the caption, thereby bringing noise into the decoding process. To produce more accurate semantic guiding information and further optimize the decoding process, we propose an end-to-end adaptive semantic-enhanced transformer (AS-Transformer) model for image captioning. For semantic enhancement information extraction, we propose a constrained weaklysupervised learning (CWSL) module, which reconstructs the semantic object's probability distribution detected by the multiple instances learning (MIL) through a joint loss function. These strengthened semantic objects from the reconstructed probability distribution can better depict the semantic meaning of images. Also, for semantic enhancement decoding, we propose an adaptive gated mechanism (AGM) module to adjust the attention between visual and semantic information adaptively for the more accurate generation of caption words. Through the joint control of the CWSL module and AGM module, our proposed model constructs a complete adaptive enhancement mechanism from encoding to decoding and obtains visual context that is more suitable for captions. Experiments on the public Microsoft Common Objects in COntext (MSCOCO) and Flickr30K datasets illustrate that our proposed AS-Transformer can adaptively obtain effective semantic information and adjust the attention weights between semantic and visual information automatically, which achieves more accurate captions compared with semantic enhancement methods and outperforms state-of-the-art methods.

Employing the BERT model for sentiment analysis of online commentary

The objective of this research is to carry out a tone and semantic sentiment analysis of network comments on new media platforms by leveraging the BERT model. With the burgeoning popularity of social media, network comments, rich in emotional and tonal features, have emerged as a significant part of the online culture. Accurate interpretation and analysis of these comments' sentiment and semantic meanings are paramount to grasping online public opinion and user psychology. In this study, the BERT model, lauded for its bidirectional encoding and contextual understanding capabilities, is selected to scrutinize the sentiment and tone of network comments on new media platforms. Through a process of pre-training and fine-tuning, the sentiment attitudes and polarity of comments are accurately identified along with their conveyed tonal features, such as joy, anger, and sarcasm. Conducting an accurate tone and semantic sentiment analysis of network comments on new media platforms facilitates a profound understanding of user preferences and trends in public opinion. This can assist in optimizing content recommendations, enhancing user experiences, and increasing the operational effectiveness of new media platforms. The outcomes of this research will bear significant implications for studies and applications in online culture, offering invaluable references and guidance in related domains.

A natural semantic metalanguage approach on the meaning of animal taboo names in the Acehnese culture

This paper utilizes the Natural Semantic Metalanguage Approach (NSM) to explore the meanings of animal-related taboo names among the Acehnese in Indonesia. Through qualitative interviews with 12 respondents from Langsa City and Peureulak District in East Aceh, the NSM approach reveals two categories of animal taboos. The first, the taboo of swearing, involves offensive language equating individuals with animals like ‘bui’ (pig), ‘asèe’ (dog), ‘pa’ee’ (gecko), ‘muruwa’ (monitor lizard), and ‘lemo’ (cow). The second, the taboo of fear, rooted in cultural beliefs, deems certain animals sacred and prohibits mentioning their names in natural settings, including ‘rimueng’ (tiger), ‘gajah’ (elephant), ‘buya’ (crocodile), ‘badeuk’ (rhinoceros), ‘uleue’ (snake), and ‘tikôh’ (rat). Acehnese use alternative names to show respect, reflecting a belief that respecting nature facilitates harmonious living. The semantic primitive meaning of animals’ taboo for swearing involves a substantive component, a substantive relational of a kind, and bad as an evaluator. Equating humans with these animals is prohibited when their names are used in the context of insulting others. The taboo of fear is attributed to the animals’ perceived holiness and sacredness, with the semantic primitive involving something kind and bad. Acehnese believe these animals possess mystical powers and can hear their names when being called, hence suggesting the use of alternative names for these animals is preferable.

CLASSIFICATION FEATURES OF ENGLISH COMPLEX SENTENCES BASED ON SYNCRETISM AND A FUNCTIONAL-SEMANTIC FIELD

Modern English is characterized by a desire to integrate various syntactic means. Syncretism is the main tool that helps neutralize asymmetric language relationships and the limited functional nature of the units. In the English language the growth of a large number of syncretic complex sentences is connected, first of all, with the desire of the speaker to convey complicated semantic meanings with the help of polyfunctional constructions where subordinate clauses function both as carriers of a single meaning and convey additional information. The development of an additional meaning occurs as a result of the semantic mobility of subordinate parts. That is, the intermediate character of the models indicates a high degree of complexity of the semantic structure and a considerable potential of semantic meaning. The purpose of the work is to investigate the internal organization of English complex sentences taking into account their structural and semantic features; to analyze the role of the influence of relevant words and connecting elements at the sentence level in order to clarify the content and linguistic aspects of complex sentences. The reliability of the analysis results is ensured by the use of various research methods and techniques. The leading methods in the work are general scientific methods (the method of observation, the method of analysis and synthesis), special methods (the comparative method, the method of component analysis, the descriptive method, the method of component and oppositional analyzes with the techniques of grouping complex sentences, the comparative method, the modeling method, functional-semantic analysis) The scientific novelty of the research lies in the fact that the paper attempts to analyze syncretic English complex sentences based on the semantic field and to describe the semantic potential of complex constructions taking into account the functional-semantic field in the English language. Conclusions. When analyzing and studying complex sentences in view of the influence of functional -semantic field and syncretism, the fact is established that the functionalsemantic fields are interconnected and realize their most significant shades of meaning precisely in the peripheral zones that testifies to the flexibility of the hypotaxis system and its dynamic nature.

Устойчивое развитие как инструмент обеспечения благополучия жизни человека (публично-правовые аспекты)

The article notes that formal articulation of sustainable development takes place in the full semantic meaning identical to that of international acts or, for lack of a better word, segmentary (primarily in terms of economic and environmental aspects). The author points out that differentiated forms of regulation of sustainable development show diversity, dynamism and ability to adapt to various legal systems. However, it is emphasized that there still remain unavoidable risks of devaluation of sustainable development. The favorable breeding ground for the origination of such risks appears due to differentiated approaches towards the value scale for the assessment of priorities of the correlation of economic growth goals, tasks and ratios, environmental policy angle, social development paths.

Partial order relation-based gene ontology embedding improves protein function prediction.

Protein annotation has long been a challenging task in computational biology. Gene Ontology (GO) has become one of the most popular frameworks to describe protein functions and their relationships. Prediction of a protein annotation with proper GO terms demands high-quality GO term representation learning, which aims to learn a low-dimensional dense vector representation with accompanying semantic meaning for each functional label, also known as embedding. However, existing GO term embedding methods, which mainly take into account ancestral co-occurrence information, have yet to capture the full topological information in the GO-directed acyclic graph (DAG). In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is critical for diverse biological tasks including computational protein annotation.

StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks

Similarity measures in heterogeneous information networks (HINs) have become increasingly important in recent years. Most measures in such networks are based on the meta path, a relation sequence connecting object types. However, in real-world scenarios, there exist many complex semantic relationships, which cannot be captured by the meta path. Therefore, a meta structure is proposed, which is a directed acyclic graph of object and relation types. In this paper, we explore the complex semantic meanings in HINs and propose a meta-structure-based similarity measure called StructSim. StructSim models the probability of subgraph expansion with bias from source node to target node. Different from existing methods, StructSim claims that the subgraph expansion is biased, i.e., the probability may be different when expanding from the same node to different nodes with the same type based on the meta structure. Moreover, StructSim defines the expansion bias by considering two types of node information, including out-neighbors of current expanded nodes and in-neighbors of next hop nodes to be expanded. To facilitate the implementation of StructSim, we further designed the node composition operator and expansion probability matrix with bias. Extensive experiments on DBLP and YAGO datasets demonstrate that StructSim is more effective than the state-of-the-art approaches.

Predicting attentional allocation in real-world environments: The need to investigate crossmodal semantic guidance.

Real-world environments are multisensory, meaningful, and highly complex. To parse these environments in a highly efficient manner, a subset of this information must be selected both within and across modalities. However, the bulk of attention research has been conducted within sensory modalities, with a particular focus on vision. Visual attention research has made great strides, with over a century of research methodically identifying the underlying mechanisms that allow us to select critical visual information. Spatial attention, attention to features, and object-based attention have all been studied extensively. More recently, research has established semantics (meaning) as a key component to allocating attention in real-world scenes, with the meaning of an item or environment affecting visual attentional selection. However, a full understanding of how semantic information modulates real-world attention requires studying more than vision in isolation. The world provides semantic information across all senses, but with this extra information comes greater complexity. Here, we summarize visual attention (including semantic-based visual attention), crossmodal attention, and argue for the importance of studying crossmodal semantic guidance of attention. This article is categorized under: Psychology > Attention Psychology > Perception and Psychophysics.

Unsupervised discovery of interpretable visual concepts

Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model's decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts. We group these similar feature patterns by clustering in “concepts”, that we visualize through Ms-IV. This last method is inspired by Occlusion and Sensitivity analysis (incorporating causality) and uses a novel metric, called Class-aware Order Correlation (CAOC), to globally evaluate the most important image regions according to the model's decision space. We compare our approach to xAI methods such as LIME and Integrated Gradients. Experimental results evince the Ms-IV higher localization and faithfulness values. Finally, qualitative evaluation of combined MAGE and Ms-IV demonstrates humans' ability to agree, based on the visualization, with the decision of clusters' concepts; and, to detect, among a given set of networks, the existence of bias.

Correlation of Semantic Understanding with Al-Qiraah An-Naqdiyah Learning Achievement

In the Arabic Education curriculum, Qiraah Naqdiyah course is about Mafhumul Maqru. Therefore, related to understanding the meaning of reading, it cannot be separated from semantic meaning (Dalalah). This research method is a quantitative method with the Pearson correlation test. This research aims to determine the correlation between understanding Dalalah and Qiraah Naqdiyah courses. The results of this research indicate is the correlation between grades in the Dalalah and the Qiroaah course both from class A and class B is in a very weak category.

ViLanIoT: A visual language for improving Internet of Things systems representation

With the rapidly evolving and changing nature of Internet of Things (IoT) technologies, there is an absence of tools to support the IoT system design and development. For example, there is not a specialized tool for representing IoT systems. The common solution to this problem is to use general-purpose modeling tools, such as the Unified Modeling Language (UML) and its extensions, which have some disadvantages when representing IoT systems. This paper proposes a new visual language, called ViLanIoT, for making representations of IoT systems conceived as cyber–physical systems. We argue that diagrams obtained with ViLanIoT are more understandable and intuitive compared to improvised representations found in literature. To illustrate the use of ViLanIoT, we represent a smart campus system with visual elements according to our proposal. In addition, we also present a study of the application of ViLanIoT. In ViLanIoT, the inclusion of visual elements for each component reflecting semantic meaning improves the understandability, clarity, and simplicity of IoT system representations.

An improved SSD lightweight network with coordinate attention for aircraft target recognition in scene videos

Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2%, 14.3%, and 10.9%, respectively, and the model parameters have been reduced by 83.9%, 73.1%, and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4%, reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces.

“Following Our Own Path”: Pavel Katenin’s Political Theater

AbstractThe present article focuses on the tension arising from Pavel Katenin’s aesthetic and literary vision for the reception of Antiquity in Russian mythological drama: his avid support of Classical purism and his denunciation of dramatists, for whom ancient myths served merely as a resource of historical parallels, is challenging to reconcile with the revolutionary conception of his play Andromache. The paper argues that Katenin regarded antiquity as an idealized universe from which numerous motifs could be drawn for inspiration. Semantic and structural means employed in the tragedy are analyzed as shedding light on the extent of Katenin’s transformation of the ancient myth to reflect the ideology of his time. The paper suggests that Classical antiquity served as a “mask,” allowing contemporary Decembrist circles to apply ancient models and situational resolutions to their own political crises. The dramatist’s conflation of masked political ideology with extreme innovation and intricate intertextual allusion is put forward as a cause of the brevity of the stage life that Katenin’s Andromache was allowed in St. Petersburg’s Bolshoi Theater.