Semantic Meaning Research Articles

Epistemic Modality in the Inaugural Speeches of Philippine Presidents

This qualitative corpus-based study employing epistemic analysis uncovered the epistemic modality (EM) markers used and their communicative functions in the seven inaugural speeches of the seven Philippine 5th Republic presidents. The findings revealed the preponderant use of epistemic modal verbs with 152 occurrences (81.72%), followed by epistemic lexical verbs with 20 occurrences (10.75%), epistemic modal adverbs with 9 occurrences (4.84%), and epistemic modal adjectives with 5 occurrences (2.69%). Moreover, the top-five modal verbs operated in the presidential inaugurals are will with 97 instances (66.9%), can with 28 instances (19.31%), shall with 9 instances (6.21%), would with 6 instances (4.14%), and could with 5 instances (3.44%). Meanwhile, should and might were never used by any president. In this regard, the presidents are confident, committed, bold, and certain with their statements; some are tentative and quite confident but still communicatively sensitive and polite; and few are uncertain but still diplomatic. Further, the communicative functions of the EM markers were categorized to high-intermediate-low modality values, certain-probable-possible semantic meanings, and close-near-distant epistemic distances.

Fine-tuning Large Language Models for Rare Disease Concept Normalization.

We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers. The second (NAME+SYN) includes HPO names and half of the concept's synonyms as well as identifiers. Subsequently, we fine-tuned Llama2 (Llama2-7B) for each sentence set and conducted an evaluation using a range of sentence prompts and various phenotype terms. When the phenotype terms for normalization were included in the fine-tuning corpora, both models demonstrated nearly perfect performance, averaging over 99% accuracy. In comparison, ChatGPT-3.5 has only ~20% accuracy in identifying HPO IDs for phenotype terms. When single-character typos were introduced in the phenotype terms, the accuracy of NAME and NAME+SYN is 10.2% and 36.1%, respectively, but increases to 61.8% (NAME+SYN) with additional typo-specific fine-tuning. For terms sourced from HPO vocabularies as unseen synonyms, the NAME model achieved 11.2% accuracy, while the NAME+SYN model achieved 92.7% accuracy. Our fine-tuned models demonstrate ability to normalize phenotype terms unseen in the fine-tuning corpus, including misspellings, synonyms, terms from other ontologies, and laymen's terms. Our approach provides a solution for the use of LLM to identify named medical entities from the clinical narratives, while successfully normalizing them to standard concepts in a controlled vocabulary.

Application of Choquet Integral-Importance-Performance Analysis and TOPSIS Methods in Approaching the Preference Factors of Calligraphy and Seal Engraving Imagery

Classical Chinese characters, presented through calligraphy, seal engraving, or painting, can exhibit different aesthetics and essences of Chinese characters, making them the most important asset of the Chinese people. Calligraphy and seal engraving, as two closely related systems in traditional Chinese art, have developed through the ages. Due to changes in lifestyle and advancements in modern technology, their original functions of daily writing and verification have gradually diminished. Instead, they have increasingly played a significant role in commercial art. This study utilizes the Evaluation Grid Method (EGM) and the Analytic Hierarchy Process (AHP) to research the key preference factors in the application of calligraphy and seal engraving imagery. Different from the traditional 5-point equal interval semantic questionnaire, this study employs a non-equal interval semantic questionnaire with a golden ratio scale, distinguishing the importance ratio of adjacent semantic meanings and highlighting the weighted emphasis on visual aesthetics. Additionally, the study uses Importance-Performance Analysis (IPA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to obtain the key preference sequence of calligraphy and seal engraving culture. Plus, the Choquet integral comprehensive evaluation is used as a reference for IPA comparison. It is hoped that this study can provide cultural imagery references and research methods, injecting further creativity into industrial design.

Interest-driven community detection on attributed heterogeneous information networks

Community structures within attributed heterogeneous information networks (AHINs) serve as valuable tools for comprehending the functional properties inherent in the real-world systems they mirror. The diverse semantics embedded in AHINs play a pivotal role in shaping distinct community formations. Many existing methods detect communities in AHINs based on the same-type nodes without specifying semantic meanings, resulting in unclear and potentially ambiguous outcomes. Additionally, by adopting a simple strategy that focuses solely on either graph structures or node attributes, they fail to sufficiently integrate heterogeneous information and maintain semantic consistency throughout the entire detection process. In this paper, we propose IDCD, a user-interest-driven community detection framework on AHINs, which groups heterogeneous nodes following specified user guidance for semantics-clear results. Firstly, we map heterogeneous nodes into one unified representation space by neatly integrating user-interest-related heterogeneous information. Then we assign pseudo community labels to nodes and refine them in a self-training way. These two processes are iteratively optimized and enhanced mutually in a unified framework. Extensive experiments demonstrate the superiority of IDCD.

Long-term student performance prediction using learning ability self-adaptive algorithm

Predicting student performance is crucial for both preventing failure and enabling personalized teaching-and-learning strategies. The digitalization of educational institutions has led to the collection of extensive student learning data over the years. Current research primarily focuses on short-term data, e.g. a single year or semester. In contrast, long-term data has the potential to offer a deeper insight into student behavior, thereby increasing the accuracy of predictions. However, the direct application of long-term data in prediction models assumes consistent data distributions over time. In the real world, evolutions in course content and structure can lead to variations in feature spaces (heterogeneity) and distribution shifts across different academic years, compromising the effectiveness of prediction models. To address these challenges, we introduce the Learning Ability Self-Adaptive Algorithm (LASA), which can adapt to the evolving feature spaces and distributions encountered in long-term data. LASA comprises two primary components: Learning Ability Modeling (LAM) and Long-term Distribution Alignment (LTDA). LAM assumes that students’ responses to exercises are samples from distributions that are parameterized by their learning abilities. It then estimates these parameters from the heterogeneous student exercise response data, thereby creating a new homogeneous feature space to counteract the heterogeneity present in long-term data. Subsequently, LTDA employs multiple asymmetric transformations to align distributions of these new features across different years, thus mitigating the impact of distribution shifts on the model’s performance. With these steps, LASA can generate well-aligned features with meaningful semantics. Furthermore, we propose an interpretable prediction framework including three components, i.e. LASA, a base classifier for outcome predictions, and Shapley Additive Explanations (SHAP) for elucidating the impact of specific features on student performance. Our exploration of long-term student data covers an eight-year period (2016-2023) from a face-to-face course at Tsinghua University. Comprehensive experiments demonstrate that leveraging long-term data significantly enhances prediction accuracy compared to short-term data, with LASA achieving up to a 7.9% increase. Moreover, when employing long-term data, LASA outperforms state-of-the-art models, ProbSAP and SFERNN, by an average accuracy improvement of 6.8% and 6.4%, respectively. We also present interpretable insights for pedagogical interventions based on a quantitative analysis of feature impacts on student performance. To the best of our knowledge, this study is the first to investigate student performance prediction in long-term data scenarios, addressing a significant gap in the literature.

Pengaruh Gairaigo Terhadap Lanskap Linguistik di Nishikasai, Tokyo

Linguistic landscape (LL) refers to the signs of linguistic objects located in public spaces as a place for human social interaction to perform a series of cultural activities. The linguistic landscape in Japan, in particular, on Nishikasai street, Tokyo, uses an absorption word called Gairaigo. This research focuses on the use of Gairaigo in the Japanese linguistic landscape, particularly in Nishikasai street, Tokyo. Through a qualitative method, the research data is drawn from the linguistic landscape along Nishikasai street, Tokyo. In the linguistic landscape of Nishikasai street, Tokyo, there are lexical semantic meanings connected with Gairaigo and there are shifts in the mention and selection of words even though the meaning and function are similar. On the other hand, the absorption of foreign language into Japanese was also found to have no change in word composition or meaning and function. The results of LL's research in Nishikasai, Tokyo showed that the criteria for the formation and use of gairaigo is based on the nuances of meaning contained in the foreign word cannot be represented by an equivalent word in Japanese; the foreign word used as Gairaigo is considered effective and efficient; and the foreign word, according to the sense of language, is considered to have good taste value, good, and harmonious as expressed by Sudjianto (2004).

The symbolic function of the portrait of Pope Innocent the X in F.M. Dostoevsky’s story “The Meek One”

RELEVANCE. Pictorial discourse is an essential element of F.M. Dostoevsky’s artistic creativity, which performs a special symbolic function and provides additional semantic load. The purpose of the study is to analyze the symbolic content of the canvas by the Spanish artist D. Velasquez “Portrait of Pope Innocent the X” (1650) in the context of the author’s idea for the story by F.M. Dostoevsky “The Meek One” (1876).MATERIALS AND METHODS. The object of the study is the novel by F.M. Dostoevsky “The Meek One”, published in the November issue of the Writer’s Diary for 1876, as well as handwritten editions and rough sketches for the story. The methodological basis of the research includes the method of comparative historical and hermeneutical-interpretative analysis. The interdisciplinary nature of the research is due to the appeal to theological, philosophical, historical and art criticism issues.RESULT AND DISCUSSION. During the research, the presence of a symbolic correlation between the portrait of the pontiff and the image of the Pawnbroker was revealed. The motives of deception, despotic domination, fear and secret voluptuousness mutually intersect and complement each other, forming a double portrait of the pope and the hero of the story. For Dostoevsky himself, the image of Pontiff Innocent X could be a kind of personification of the Catholic institution of the Papacy and the tendencies hidden in it towards spiritual despotism and absolute domination over man. A vivid artistic device, which was never realized in the final version of the story, acquires special significance and sound in the context of Dostoevsky’s anti-Catholic polemic. CONCLUSION. The symbolic function of the papal portrait has an important conceptual significance, both within the author’s idea of the story “The Meek One” and beyond it in the context of Dostoevsky’s anti-Catholic discourse. The obtained conclusions make important adjustments to the further study of the story by F.M. Dostoevsky, deepening its semantic meaning.

Multimodal Content Analysis Using Deep Learning

Abstract: The multimodal content analysis platform combines sentiment analysis and neural style transfertechniques to process and improve various types of digital content. The sentiment analysis module utilizes natural language processing (NLP) algorithms, such as recurrent neural networks (RNNs) or transformer models like BERT, to extract emotional signals from textual, visual, and auditory inputs. Signals are classified into predefined sentiment categories, providing granular insights into the emotional context of the content. The platform employs neural style transfer algorithms, such as style transfer networks (NSTNs) or generative adversarial networks (GANs), to transfer stylistic attributes between texts. By training on a diverse range of artistic styles, the system learns to apply these styles to input text while preserving semantic meaning. This process enhances the visual representation of textual content, making it more appealing and engaging to users.

Decoding Phonetic Meaning in a Semantically Meaningful Lexicon: Sound Symbolism in Steam Game Titles

It is widely believed that the marriage between a word’s sound and meaning is not an arranged one. A major exception to this, however, is sound symbolism. Inspired by previous research on the extensive use of sound symbolic associations in naming practices, this study directed attention towards the titles of popular games on Steam, which, unlike brand names, are mostly composed of semantically meaningful lexical items. The results showed that Action/Adventure/RPG game titles contained more voiced obstruents and fewer low vowels than Strategy/Simulation game titles. Additional research could delve into the interplay between semantic and phonetic meanings conveyed by names and titles.

PreLog: A Pre-trained Model for Log Analytics

Large-scale software-intensive systems often produce a large volume of logs to record runtime status and events for troubleshooting purposes. The rich information in log data enables a variety of system management and diagnosis tasks. Over the years, many approaches have been proposed for automated log analytics. However, these approaches usually design separate models for each specific task, which cannot be generalized to other tasks. They are also not robust when dealing with logs from heterogeneous sources. In this paper, we propose PreLog, a novel pre-trained model for log analytics. PreLog is pre-trained on a large amount of unlabelled log data to capture the semantic meaning of logs. We design two log-specific pre-training objectives, including entry-level and sequence-level objectives, which enable PreLog to better understand the hidden structure and semantics of logs. To perform downstream log analytics tasks, we leverage a prompt tuning paradigm to convert downstream tasks' objectives into a similar form as the pre-training stage. We have conducted extensive experiments on two main log analytics tasks (i.e., log parsing and log-based anomaly detection). Experimental results show that PreLog achieves better or comparable results in comparison with the state-of-the-art, task-specific approaches. PreLog is cost-effective and can be uniformly applied to many log analytics tasks through the prompt tuning paradigm.

Lexical Relations in Joe Biden's Speech about Plan to Lower Prescription Drugs Cost: Semantics Study

The purpose of this research is to analyze the types of lexical relations and the types of meanings of lexical relations that appear in Joe Biden's Speech about the plan to reduce prescription drug prices. The method used in this research is qualitative method. The results of this research show that there are eight types of lexical relations and three types of semantic meanings; the types of lexical relations show that from 30 data that have been analyzed, eight synonym data (26.7%), ten antonym data (33.3%), three meronym data (10%), two polysemy data (6.7%), two hyponym data (6.7%), two member-collection data (6.7%), two portion-mass data (6.7%), and one homonym data (3.3%). Meanwhile, the types of semantic meanings found from 60 data that have been analyzed are 51 data of literal meaning (85%), 5 data of semi-idiomatic meaning (8.3%), and 4 data of fully idiomatic meaning (6.7%). Therefore, it can be concluded that the type of lexical relations that appears the most is antonyms at 33.3% and that the most common type of meaning is literal meaning, which is 85%. Keywords: Lexical relations, Semantics, Speech

Late better than early: A decision-level information fusion approach for RGB-Thermal crowd counting with illumination awareness

In this paper, we make the first research effort to address the RGB-Thermal (RGB-T) crowd counting problem with the decision-level late fusion manner. Being different from the existing pixel-level or feature-level fusion methods, our proposition chooses to fuse the density maps yielded by RGB and thermal counterparts via spatially adaptive weighting with RGB illumination-aware attention. Our key intuition to conduct RGB-T density map fusion lies in 2 main folders. First, compared with the raw RGB-T images or convolutional feature maps, RGB-T density maps contain stronger counting-wise semantic meanings. Secondly, they are also of high spatial resolution for revealing fine local details. To fuse them adaptively, a spatial weighting map for each modality, together with an illumination-related RGB weight is generated. In this way, the issues of RGB illumination awareness and local counting pattern characterization ability are concerned jointly. To the best of our knowledge, we are the first to leverage RGB-T crowd counting concerning these 2 issues in a unified way. Meanwhile, cross-modality feature interaction is conducted between RGB and thermal modalities to facilitate spatial weighting map generation. The experiments on 2 well-established RGB-T crowd counting datasets (i.e., RGBT-CC and DroneRGBT) verify the superiority of our proposition. The source code and pretrained models will be released upon acceptance at https://github.com/hustaia/DLF-IA.

SEMANTICS OF THE CONCEPT OF “STEED”/“HORSE”IN THE RUSSIAN ART OF THE FIRST THIRD OF THE20TH CENTURY: “A HORSE AS A HORSE” AND “WE AREALL A LITTLE BIT HORSES”

The article continues the research on the artistic image in the Russian art of the first third of the 20th century and aims at studying the concepts of “steed”/“horse” in the art of the 1900s-1920s. From the beginning of revolutionary transformations in Russian art to the changes after 1917, the semantic meaning of the artistic image in its evolution is of particular interest. The basis for the study is largely fiction, in which the horse image was formed most fully. The multidimensionality of semantic relations within the boundaries of the synonymic paradigm with the general meaning of “horse” is examined in the article on the material of fine arts. The author studies the diversity of artistic roles, symbolic and semantic functions, which were given to the horse image in the art of a certain period and reveals the specificity of these roles depending on the artistic concept. Identification and recognition of the image occurs at different levels. The content of the concept is not revealed only through the plot. Unexpected semantic associations, suggested to the artist by visual, worldly, intellectual, emotional experience or creative intuition, are also possible. In this context, the quotations in the title of the article mark two main notions: “a horse as a horse” (V. Shershenevich) actualizes the direct meaning of “a horse as an animal”, and “we are all a little bit horses” (V. Mayakovsky) is a zoometaphor defining the inner state of a person. At this stage of the study, we can distinguish five main sections that allow us to structure and order the set of works containing the horse image: a village, a city, a rider, Apocalypse, a historical event (here the Civil War and collectivization). In order to fully understand the specifics of the semantic content of the horse image in the art during the study period, it is necessary to carry out some additional research. So far, the proposed text cannot claim to be an exhaustive solution to the problem, but outlines the vectors that allow us to continue its study.

شُهُورُ السَّنَةِ: دراسةٌ لُغويةٌ

The months of the year had names in pre-Islamic era that were replaced by the emergence of Islam. The order of the months is from the month of Muharram to the month of Dhul-Hijjah. Ramadan is the only month the wording and semantic meaning of which are mentioned in the Qur’an. Months are reduced, and attributed to, as in the case with names. In addition, months can come in dual and plural forms, and all months are linguistically masculine except for Jumada which is linguistically feminine. All months are genitive except for Shaban and Ramadan due to them being nouns and them including 'alif' and 'noon' letters. All of this lead to this study which is presented in an introduction with two foci points that include the definition of the months, and the definition of the year and its synonyms. Following this, a lexical-morphological study of the months of the year is presented. After that, a semantic study of the months of the year is presented. To end with, a conclusion is drawn and is followed up by the list of references.

A Study on the Semantics of the Sichuan Dialect Word Ba Based on the Cognitive Perspective

The Sichuan dialect word ba is widely used and has a high frequency of occurrence within the dialect area. It is characterized by its rich semantic meanings and diverse parts of speech, making it one of the most representative common words in Sichuan dialect. However, existing dialect dictionaries have issues such as incomplete definitions, unclear interpretations, and uncertain grammatical properties, which call for further careful revisions. Cognitive linguistics, one of the current research hotspots in linguistics, can fundamentally explain the intrinsic mechanisms and external manifestations of lexical semantic formation. This paper, based on the cognitive perspective and within the macro-framework of Prototype Category Theory, employs the theories of metaphor, metonymy, and image schema to conduct a micro-level analysis and clarification of the original meaning and extended meanings of the Sichuan dialect word ba. It attempts to provide cognitive reasoning for interpreting the meanings of dialect words in dictionaries, as well as to offer new perspectives and cases for exploring the semantics of dialect words.

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods.

Epistemic Modality in the Inaugural Speeches of Philippine Presidents

This qualitative corpus-based study employing epistemic analysis uncovered the epistemic modality (EM) markers used and their communicative functions in the seven inaugural speeches of the seven Philippine 5th Republic presidents. The findings revealed the preponderant use of epistemic modal verbs with 152 occurrences (81.72%), followed by epistemic lexical verbs with 20 occurrences (10.75%), epistemic modal adverbs with 9 occurrences (4.84%), and epistemic modal adjectives with 5 occurrences (2.69%). Moreover, the top-five modal verbs operated in the presidential inaugurals are will with 97 instances (66.9%), can with 28 instances (19.31%), shall with 9 instances (6.21%), would with 6 instances (4.14%), and could with 5 instances (3.44%). Meanwhile, should and might were never used by any president. In this regard, the presidents are confident, committed, bold, and certain with their statements; some are tentative and quite confident but still communicatively sensitive and polite; and few are uncertain but still diplomatic. Further, the communicative functions of the EM markers were categorized to high-intermediate-low modality values, certain-probable-possible semantic meanings, and close-near-distant epistemic distances.

Detection of Malicious URLs using Machine Learning based on Lexical Features

As the digital world evolves, the risk of valuable information being exposed to unauthorized parties is increasing. One common vulnerability is the use of malicious Uniform Resource Locator (URL), which are fraudulent links spread across various platforms such as social media and emails. Traditional methods of identifying these URLs, such as blacklisting and heuristic search, rely heavily on syntax or keyword matching, but struggle to keep up with the evolving tactics of cyber attackers. Hence, this paper proposes a solution for detecting malicious URLs and their types based on lexical features. Lexical features in a URL refer to the components that convey semantic and lexical meaning. These can include domain names, path lengths, special characters, and other elements that can be analyzed for patterns or anomalies. In our proposed method, we use 23 different lexical features that focus on the semantic and lexical meaning of the URLs. An Exploratory Data Analysis (EDA) is used to filter the most important lexical features that effectively contribute to predictions. With these carefully curated features, we address the problem as a multi-classification task, aiming to assess the performance of three distinct classifiers: Random Forest, which currently stands as the domain's best solution and a pure bagging technique, as well as XG Boost and Light GBM, both of which utilize boosting techniques. With the proposed method, we could achieve over 93\% accuracy for all the three classifiers while Random Forest achieving the best performance.

Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.

Leveraging AI for synthesizing the deluge of biomedical knowledge has great potential for pharmacological discovery with applications including developing new therapeutics for untreated diseases and repurposing drugs as emergent pandemic treatments. Creating knowledge graph representations of interacting drugs, diseases, genes, and proteins enables discovery via embedding-based ML approaches and link prediction. Previously, it has been shown that these predictive methods are susceptible to biases from network structure, namely that they are driven not by discovering nuanced biological understanding of mechanisms, but based on high-degree hub nodes. In this work, we study the confounding effect of network topology on biological relation semantics by creating an experimental pipeline of knowledge graph semantic and topological perturbations. We show that the drop in drug repurposing performance from ablating meaningful semantics increases by 21% and 38% when mitigating topological bias in two networks. We demonstrate that new methods for representing knowledge and inferring new knowledge must be developed for making use of biomedical semantics for pharmacological innovation, and we suggest fruitful avenues for their development.

A semantic study of "heavy" and its corresponding Chinese word "zhong"

This paper aims to analyze the English adjective "heavy" and its corresponding word in Chinese, "zhong", using the framework of lexical typology as suggested by François (2008). Through this article, we gain a comprehensive understanding of the various semantic meanings associated with "heavy" in English. These semantic meanings are derived from empirical observations and functional properties. Moreover, we compare these meanings with their corresponding counterparts in Chinese, revealing both similarities and differences with the word "zhong". In the English language, "heavy" is connected to several senses, as defined by the Oxford Dictionary. It can refer to something weighing a lot, being worse than usual, not delicate when modifying, being thick as a material, being full of something, being large and powerful when modifying machines, being busy, or being involved in physically demanding work (heavy digging/lifting). Additionally, "heavy" can modify actions like "fall" or "hit", describe a substantial amount of food, indicate excessive use, modify sounds, imply seriousness or difficulty, describe large bodies of water (e.g., sea/ocean), and pertain to weather conditions, air, and soil. Furthermore, it can connote strictness in certain contexts. Several of these meanings find parallels in the Chinese word "zhong", such as referring to something that weighs a lot, modifying machines to signify size and power, relating to physically demanding work (e.g., heavy digging/lifting), describing the fall or impact of objects, indicating a substantial amount of food, or denoting seriousness or difficulty. Moreover, both "heavy" and "zhong" share the function of modifying actions related to drinking, smoking, or sleeping. However, it is important to note that in Chinese, alternative words like "chen" or "si" can also be used to express similar ideas.