Semantics Of Language Research Articles

A study on intelligent translation of English sentences by a semantic feature extractor

Abstract In order to enhance the performance of machine translation, this article briefly introduced algorithms that can be used to extract semantic feature vectors. Then, the aforementioned algorithms were integrated with the encoder–decoder translation algorithm, and the resulting algorithms were subsequently tested. First, the performance of the semantic recognition of the long short-term memory (LSTM)-based semantic feature extractor was tested, followed by a comparison with the translation algorithm that does not include semantic features, as well as the translation algorithm that incorporates convolutional neural network-extracted semantic features. The findings demonstrated that the LSTM-based semantic feature extractor accurately identified the semantics of the source language. The proposed translation algorithm, which is based on LSTM semantic features, achieved more accurate translations compared to the other two algorithms. Furthermore, it was less affected by the length of the source language.

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Traditional design methods heavily rely on domain expert knowledge using documents to define assembly solutions which are later verified through simulations. However, these document-centric approaches cannot provide graphical notations for engineers to efficiently understand the entire assembly process. Moreover, it is difficult to analyze the performance of the designed assembly processes using simulations since the simulation models have to be developed based on the documents manually rather than be generated automatically from the design models. In this paper, a semantic-driven approach is proposed to support aircraft assembly process formalism and performance analysis. First, meta-models of aircraft assembly processes are developed based on SysML and discrete-event simulation models using a semantic modeling language named KARMA. Then an application ontology is defined for generating semantic models from KARMA architecture models to capture domain knowledge, system requirements and simulation model information of the aircraft assembly process. A model transformer is developed to transform the KARMA models to discrete-event simulation models based on the application ontology. Then the generated simulation models are executed to obtain the simulation results for verifying the designed assembly process. Finally, the obtained simulation results are used to support decision-making of selecting the optimal aircraft assembly process. A case study is conducted to verify the proposed method.

Eventive modal projection: the case of Spanish subjunctive relative clauses.

How do modal expressions determine which possibilities they range over? According to the Modal Anchor Hypothesis (Kratzer in The language-cognition interface: Actes du 19e congrès international des linguistes, Libraire Droz, Genève, 179-199, 2013), modal expressions determine their domain of quantification from particulars (events, situations, or individuals). This paper presents novel evidence for this hypothesis, focusing on a class of Spanish relative clauses that host verbs inflected in the subjunctive. Subjunctive in Romance is standardly taken to be licensed only in a subset of intensional contexts. However, in our relative clauses, subjunctive is exceptionally licensed in extensional contexts. At the same time, the interpretation of these relative clauses still involves modality, a type of modality that targets the goals of the agent of the main event. We argue that the pattern displayed by these relative clauses follows straightforwardly if subjunctive is associated with a modal operator that, like modal indefinites (Alonso-Ovalle and Menéndez-Benito in Journal of Semantics 35(1):1-41, 2017), can project its domain from a volitional event. Overall, our proposal supports the event-based analysis of mood (Kratzer in Evidential mood in attitude and speech reports. Talk delivered at the 1st Syncart Workshop, Siena, July 13, 2016; Portner and Rubinstein in Natural Language Semantics 28:343-393, 2020) and extends its application beyond attitudinal and modal complements.

Revisiting technology-assisted language learning affordances for language components: a decade survey

ABSTRACT Given the important technology-assisted language learning (TALL)-empowered affordances, a comprehensive understanding of how these TALL tools facilitate learning across different language components, e.g., phonology, morphology, syntax, semantics, and pragmatics, could not only present the panoramic scenery of the subject matter but also inform pedagogy for applied linguistics researchers and practitioners. Despite the increasing attention being paid to the affordances of a particular TALL platform, there is still a lack of more holistic research practice to the affordances for the five language components. To bridge the gap, this study aims to systematically and quantitively revisit the TALL-empowered affordances with respect to these language components. To this end, a total of valid 201 articles published from 2010 to 2023 were selected and coded based on the proposed inclusion/exclusion criteria. Results revealed that different TALL platforms (viz. computer, mobile, tablet, virtual reality, and robot) help improve students’ language learning performance and efficacy among the five language components. The implications of the study, in perfect tune with the distance language learning experiences in the post COVID-19 pandemic era, justify the need to take full advantage of the TALL affordances and accelerate the move toward a more technology-friendly field of study.

Auditory naming is impaired in posterior cortical atrophy and early-onset Alzheimer's disease.

Visual naming ability reflects semantic memory retrieval and is a hallmark deficit of Alzheimer's disease (AD). Naming impairment is most prominently observed in the late-onset amnestic and logopenic variant Primary Progressive Aphasia (lvPPA) syndromes. However, little is known about how other patients across the atypical AD syndromic spectrum perform on tests of auditory naming, particularly those with primary visuospatial deficits (Posterior Cortical Atrophy; PCA) and early onset (EOAD) syndromes. Auditory naming tests may be of particular relevance to more accurately measuring anomia in PCA syndrome and in others with visual perceptual deficits. Forty-six patients with biomarker-confirmed AD (16 PCA, 12 lvPPA, 18 multi-domain EOAD), at the stage of mild cognitive impairment or mild dementia, were administered the Auditory Naming Test (ANT). Performance differences between groups were evaluated using one-way ANOVA and post-hoc t-tests. Correlation analyses were used to examine ANT performance in relation to measures of working memory and word retrieval to elucidate cognitive mechanisms underlying word retrieval deficits. Whole-cortex general linear models were generated to determine the relationship between ANT performance and cortical atrophy. Based on published cutoffs, out of a total possible score of 50 on the ANT, 56% of PCA patients (mean score = 45.3), 83% of EOAD patients (mean = 39.2), and 83% of lvPPA patients (mean = 29.8) were impaired. Total uncued ANT performance differed across groups, with lvPPA performing most poorly, followed by EOAD, and then PCA. ANT performance was still impaired in lvPPA and EOAD after cuing, while performance in PCA patients improved to the normal range with phonemic cues. ANT performance was also directly correlated with measures of verbal fluency and working memory, and was associated with cortical atrophy in a circumscribed semantic language network. Auditory confrontation naming is impaired across the syndromic spectrum of AD including in PCA and EOAD, and is likely related to auditory-verbal working memory and verbal fluency which represent the nexus of language and executive functions. The left-lateralized semantic language network was implicated in ANT performance. Auditory naming, in the absence of a visual perceptual demand, may be particularly sensitive to measuring naming deficits in PCA.

Quranic Studies Evolution: A Bibliometric Analysis from 1880 to 2023

This analysis focuses on the study of the Quran, from 1880 to 2023 using data obtained from Scopus. Made accessible through a BibTeX file on GitHub. The examination covers aspects of research trends including the number of citations per year the influence of sources notable authors and their affiliations the countries of corresponding authors the production trends in different countries over time and a network illustrating how key concepts are interconnected. By conducting an analysis several significant findings were discovered. The data demonstrated an increase in citations per year indicating a growing importance and scholarly interest in Quranic Studies. Noteworthy authors such as Rippin A and Reynolds GS as prominent affiliations like the University of Malaya and International Islamic University Malaysia made substantial contributions to this field. The distribution of corresponding authors countries highlighted Malaysias, Indonesias and Irans roles. Furthermore when examining production trends over time for countries it became evident that contributions from the United Kingdom, USA,Malaysia,Iran and Indonesia had seen growth. Additionally the interconnectedness between concepts such as natural language processing systems, computational linguistics,semantics and information retrieval were emphasized in the cooccurrence network, within Quranic Studies. This, in depth analysis provides insights into the development significant contributors and intersections of themes in the study of the Quran. It establishes a foundation for research endeavors, in this area.

Semantically triggered qualitative simulation of a geological process

The field of geology has been the subject of a range of research efforts aiming to formalize geological domain knowledge, notably through geological domain ontologies. The main focus of existing geological ontologies primarily lies in describing static geological objects and their properties, paying less attention to the knowledge concerning geological processes and events. Meanwhile, the geological process modeling and simulation predominantly rely on quantitative numerical approaches that necessitate comprehensive and abundant data as input. However, many geological processes took place on a million-year time scale with insufficient data and non-direct observations. Given the inherent incompleteness of geological data, geologists still rely on qualitative reasoning to validate their interpretations. There is currently a dearth of applicable methods to facilitate qualitative reasoning and simulate geological processes based on domain knowledge.We propose to model the effects of a geological process through an object-oriented program, while keeping an ontological representation of the situation at each instant. To combine the two models, we propose using semantically defined ‘process triggers.’ These process triggers are defined as part of the ontology, in accordance with the Basic Formal Ontology. They enable geologists to describe the precise moment when a geological process is triggered and initiated. On the computational program side, we employ the ‘Semantic Micro Object Language’ to embody the knowledge and rules provided by geologists, facilitating the simulation of geological processes. Through an evaluation experiment, our proposed approach demonstrates promising results within a reasonable timeframe. As proof of concept, we have applied our method to a real-world scenario of petroleum thermal maturation in Ekofisk Field and got a promising result. Our approach provides a formalism that allows a powerful code to interact with domain ontologies, which paves the path for future knowledge reasoning.

An Iris Instance for Verifying CompCert C Programs

Iris is a generic separation logic framework that has been instantiated to reason about a wide range of programming languages and language features. Most Iris instances are defined on simple core calculi, but by connecting Iris to new or existing formal semantics for practical languages, we can also use it to reason about real programs. In this paper we develop an Iris instance based on CompCert, the verified C compiler, allowing us to prove correctness of C programs under the same semantics we use to compile and run them. We take inspiration from the Verified Software Toolchain (VST), a prior separation logic for CompCert C, and reimplement the program logic of VST in Iris. Unlike most Iris instances, this involves both a new model of resources for CompCert memories, and a new definition of weakest preconditions/Hoare triples, as the Iris defaults for both of these cannot be applied to CompCert as is. Ultimately, we obtain a complete program logic for CompCert C within Iris, and we reconstruct enough of VST's top-level automation to prove correctness of simple C programs.

Семантика неполноты действия в русском, украинском и английском языках

Semantic categories can be studied effectively using a comparative approach, since it allows to determine the general and specific features of the object under study against the background of the languages under consideration, but also to see the peculiarities of native speakers’ perception of the surrounding world. This is also relevant for the category of aspectuality. The discoveries of Slavic aspectology of the 20th century were actively used to study aspectual semantics in other Indo-European languages, including Germanic ones, in which the category of aspectuality has a different character and structure. Some questions in this field still remain controversial, with certain aspectual meanings requiring further study. This research focuses on the semantics of incomplete action in verbs and verb combinations of the attenuative mode of action in the Russian, Ukrainian, and English languages. The type of aspectual meaning under consideration is related to the idea of a partially complete action, action lasting for a short period of time, or insufficient to obtain a full result. Prefixal verbs with diminutive and related meanings are numerous in Slavic languages, which is explained by a developed verbal prefixation in general and special features of speakers of Slavic languages. A comparison of “attenuative” verbs and verbal units in Russian, Ukrainian, and English makes it possible to identify the main meanings of incomplete action in these languages and assess the productivity of their formation patterns in the compared languages. The study has demonstrated the variety of means to express the incompleteness of an action (prefixes, suffixes, adverbial combinations, etc.) in the compared languages, as well as the synthetic way of expressing the specified semantics in Russian and Ukrainian and the analytical way in English. This is explained both by the predominantly analytical structure of English as a whole, and by the likely lesser significance of attenuative meanings for English speakers when actualizing the inner parameters of the action. The analysis has demonstrated the following meanings of the studied units in the languages under consideration: 1) incompleteness of an action and its lesser result; 2) limited action in terms of its duration; 3) simultaneous incompleteness of an action and its complementarity, an addition of something to it; 4) lesser degree of an action, its sporadic character. Individual aspects of the meanings of incompleteness in compared languages do not coincide and and are in the relations of intersection.

Модально-компаративные конструкции с семантикой кажимости в тувинском языке

The paper examines analytical, synthetic, and lexical means of expressing modal-comparative semantics in the Tuvan language. Complex analytical constructions encompass constructions containing the first participial component, modal operators deg ‘as if,’ yshkash ‘as if,’ and the verb bol ‘to be’ in the adverbial form on -(Y)p: boop. Simple synthetic forms include the verb predicate with the affix -ZYg. Lexical means include verbs denoting pretended behavior, also characterized by the meaning of a hypothetical comparison, forming the foundation for the semantics of pretense. Constructions with modal operators deg ‘as if’, yshkash ‘as if,’ and the predicate with the affix -ZYg are used to express a comparison with an imaginary standard (an unreliable comparison). The construction with the word boop and special verbs of pretended behavior are also distinguished by a comparative meaning in their semantics. Analytical and synthetic structures containing modal operators of appearance indicate the inclusion of the perceiving subject’s worldview and personal experiences in linguistic reflection. The meaning of an unreliable comparison is the basis for the formation of the meaning of seemingness. The construction Tv=GAn + yshkash/deg conveys the semantics of seemingness without any additional nuances. Verb forms with the affix -ZYg express the semantics of seemingness based on the speaker’s emotions and imagination. The construction Tv=GAn + boop identifies the object of comparison with the standard and expresses an apparent feigned simulative action.

Analysis of research into the mathematical foundations of model-based systems engineering

The article provides an analysis of research into the mathematical foundations of model-based systems engineering (MBSE, Model-Based Systems Engineering). Both the classical mathematical theory of Wymore’s system design and modern research are considered, in particular, the formalization of the semantics of the SysML language using automata theory, the use of category theory as a formal mathematical basis for model-based system design, the study of the possibilities of combining the theoretical basis of Wymore’s systems and the universal Discrete Event System Specification (DEVS) modeling formalism.

The new language corpus: Exploring perception of graphemes "P" and "B" in Serbian Cyrillic and Latin scripts according to semantical context

Language corpus serves as an essential instrument in facilitating research endeavors within the domain of natural language processing. As an illustration of such a language corpus, the paper presents part of the conducted research. Notably, the Serbian language demonstrates a deficiency in its linguistic resources, a concern accentuated by its unique status as one of the few languages characterized by a bialphabetical system, i.e., although in official use only the Cyrillic alphabet is used, in the Serbian language the Latin alphabet is also used in parallel. The Serbian Cyrillic alphabet in modern times is threatened by various influences of globalization, i.e., the more dominant use of the Latin alphabet in everyday life. The primary goal of the research is to examine the perceptual differences between the Latin and Cyrillic alphabets. For the purposes of the research, a sample of subjects whose native language is not Serbian was used, so that the subjects would not be compelled by previous knowledge of the semantics of the Serbian language. The subjects would have to know at least the basics of the Serbian language and the rules of reading and writing to be able to understand the questionnaire, simplified with vocabulary for level A2. Within this paper, a segment of this research will be presented through several compared variables and predominantly by examining whether the graphemes "P" and "B" can cause confusion because these graphemes in Serbian Cyrillic and Latin have different phonetic pronunciations. The research showed that the answers given by the subjects were in a higher percentage perceived in the reverse script of the questionnaire (if the questionnaire was in Cyrillic, the perception of unclear words by a higher percentage of subjects was in Latin, and vice versa).

The concepts of “Motherland” and “State” in Russian language

The subject of the study is the concepts “Motherland” and “State”, their synonyms and associative images in Russian songs and Russian language, including Russian phraseological units, proverbs and sayings. The first concept is understood as a small homeland and a large homeland. Each person has his own small homeland, which is associated with place of birth, family, house, street, village, town, etc. The large homeland in a broad sense refers to the native country. The small homeland is part of the big homeland. These concepts play a role in the formation of Russian person’s national consciousness, spiritual values, worldview, character and personality. The purpose of this work is to clarify their meanings and differences in Russian language and in Russian people’s consciousness. Research methods include descriptive, associative, comparative methods, methods of synthesis and generalization, and semantic analysis. When analyzing and comparing the concepts “Motherland” and “State”, this work examines the images reflected in the texts of Russian songs about these concepts and language examples in Russian phraseological dictionaries. The conducted research shows that the term “Motherland”, associated with national history, culture and ethnic identity, has a spiritual psychological flavor. The term “State” with a social meaning is defined as a political, legal, economic form of organization of human society. This concept is associated with the images of “law”, “authority”, “army/armed force”, “citizen”, etc. Clarifying the differences in their semantics in Russian language helps speakers of other linguistic cultures to better understand their significance in Russian people’s history, culture and consciousness, their role and influence on the formation of Russian people’s national psychology and spiritual world. The study of these concepts has prospects for expanding foreign students' vocabulary, deeper understanding of the Russian language, culture, people, its spiritual world and their relationships and enriching their cultural, linguistic and personal experience.

Stable word complexes of comparative semantics with a component-demonym in the Swedish and Russian languages

The article is devoted to identifying of general patterns and distinctive features in the structure, meaning and evaluation of stable verbal complexes of comparative semantics of the Swedish and Russian languages with a component-demonym. Stable verbal complexes of comparative semantics with a demonic component are present in both the Swedish and Russian languages. There is a significant quantitative disproportion between stable comparisons and paremias of comparative semantics of both languages, but in the Swedish language in favor of proverbs, and in Russian — in favor of stable comparisons. It can be assumed that the names of evil spirits are more taboo in Swedish stable comparisons, and also comparative phraseological units in general were less subject to lexicographic fixation. The studied proverbs of the Swedish and Russian languages are quantitatively comparable. Structurally, the similarity of the proverbs of the two languages is manifested in the presence of such models as sentences with opposition introduced by adversative conjunctions, and sentences with the presence of a component in the comparative degree. But the analyzed Swedish proverbs are characterized by such models as sentences with adjacent components, identity sentences and sentences built according to the syntactic model “better… than…”, which are absent in the studied material of the Russian language. In turn, in the analyzed Russian proverbs one can note models that are absent in Swedish ones: sentences built on the principle of syntactic parallelism and collapsed comparison.

Self-Supervised Representation Learning With Spatial-Temporal Consistency for Sign Language Recognition.

Recently, there have been efforts to improve the performance in sign language recognition by designing self-supervised learning methods. However, these methods capture limited information from sign pose data in a frame-wise learning manner, leading to sub-optimal solutions. To this end, we propose a simple yet effective self-supervised contrastive learning framework to excavate rich context via spatial-temporal consistency from two distinct perspectives and learn instance discriminative representation for sign language recognition. On one hand, since the semantics of sign language are expressed by the cooperation of fine-grained hands and coarse-grained trunks, we utilize both granularity information and encode them into latent spaces. The consistency between hand and trunk features is constrained to encourage learning consistent representation of instance samples. On the other hand, inspired by the complementary property of motion and joint modalities, we first introduce first-order motion information into sign language modeling. Additionally, we further bridge the interaction between the embedding spaces of both modalities, facilitating bidirectional knowledge transfer to enhance sign language representation. Our method is evaluated with extensive experiments on four public benchmarks, and achieves new state-of-the-art performance with a notable margin. The source code is publicly available at https://github.com/sakura/Code.

Effective Approach for Fine-Tuning Pre-Trained Models for the Extraction of Texts From Source Codes

This study introduces SR-Text, a robust approach leveraging pre-trained models like BERT and T5 for enhanced text extraction from source codes. Addressing the limitations of traditional manual summarization, our methodology focuses on fine-tuning these models to better understand and generate contextual summaries, thus overcoming challenges such as long-term dependency and dataset quality issues. We conduct a detailed analysis of programming language syntax and semantics to develop syntax-aware text retrieval techniques, significantly boosting the accuracy and relevance of the texts extracted. The paper also explores a hybrid approach that integrates statistical machine learning with rule-based methods, enhancing the robustness and adaptability of our text extraction processes across diverse coding styles and languages. Empirical results from a meticulously curated dataset demonstrate marked improvements in performance metrics: precision increased by 15%, recall by 20%, and an F1 score enhancement of 18%. These improvements underscore the effectiveness of using advanced machine learning models in software engineering tasks. This research not only paves the way for future work in multilingual code summarization but also discusses broader implications for automated software analysis tools, proposing directions for future research to further refine and expand this methodology.

Relevance of the Retrieval of Hadith Information (RoHI) using Bidirectional Encoder Representations from Transformers (BERT) in religious education media

This research explores the impact of integrating Bidirectional Encoder Representations from Transformers (BERT) into the Retrieval of Hadith Information (RoHI) application within the realm of religious education media. Hadith, the sayings and actions of Prophet Muhammad, play a pivotal role in Islamic teachings, requiring accurate and contextually relevant retrieval for educational purposes. RoHI, designed to enhance access and comprehension of Hadith literature, employs BERT's advanced natural language processing capabilities. The study assesses how BERT-enhanced RoHI facilitates efficient retrieval and interpretation of Hadith texts. By leveraging BERT's ability to capture intricate language patterns and semantics, the study aims to enhance the precision and contextual appropriateness of retrieved Hadith information. The study also discusses implications for digital learning platforms, emphasizing the potential of NLP technologies to foster broader access to religious knowledge and promote inclusive educational practices. This research contributes to the field by proposing a framework that integrates advanced AI techniques with religious education, ensuring that learners receive accurate and meaningful Hadith information tailored to their educational needs. The findings highlight the potential of BERT in revolutionizing information retrieval processes in religious studies, paving the way for more effective educational tools and resources in Islamic learning environments.

A Semi-Automated Approach to Process Discovery from Multiple Natural Language Documents

The discovery of process information in natural language documents is hindered by the innate ambiguity of natural language. This issue is even more challenging when process information is spread throughout several documents, each with the possibility of different formats, writers, and original purposes, that must be read and interpreted individually by a business analyst before their information can be uncovered. This work presents a semi-automated approach to process discovery from multiple documents simultaneously, making use of semantic similarity measures and natural language processing techniques to identify, gather, and extract process information from these documents. An experiment is conducted with this approach to demonstrate its use and results.

Loanwords and polysemy: An investigation of specialized domain lexis in Middle English

This study investigates the polysemy of French loanwords in Middle English, analysing the attested senses of loan lexis in three occupational domains. Senses recorded in the Dictionnaire du moyen français and the Anglo-Norman dictionary were compared with corresponding lexical entries in the Middle English dictionary. Out of over 100 Middle English items borrowed from French, it is found that the great majority were polysemous in ways reflecting the source language semantics. This outcome contrasts with the usual finding that foreign borrowing in modern contexts produces monosemous loanwords. The conclusion is therefore drawn that the process of borrowing from French was of a different nature in the English medieval period. It is argued that bilingual users of French were agents of borrowing who replicated source word polysemy in their English. The likelihood that senses matching those of the French etymon arose in Middle English independently of source language polysemy is considered, but rejected.

Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic inference reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use feature engineering to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words highly relevant to the inference keyword received significantly more eye fixations per word: 1.0584 compared to 0.6576, including words with no fixations. This study represents the first attempt to classify brain states at a word level using LLM-generated labels. It provides valuable insights into human cognitive abilities and Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.