Words In Context Research Articles

Sachant que : un marqueur (plus ou moins juste) de mémoire sémanticodiscursive

A construction with epistemic value, sachant que is a discourse marker that covers both semantic-logical and discursive-argumentative values. Our paper sets out to follow the behaviour of this marker through a heterogeneous corpus, from the point of view of textual typology (literary texts, everyday discourse) but also in specialised language, and more specifically legal language (written and oral). The interest in analysing this marker lies in the fact that its uses, which are very frequent in contemporary French, tend to diluteitsinitialepistemic value,which refers to a known content shared by the enunciative instances. Our analysis follows the mechanism of weakening or maintaining thesemantic and argumentativeforce of this discursive unit through various types of discourse, in order to identify the correct use of the word in approprithrough various types of discourse, in order to identify the correct use of the word in appropriate contexts and with the appropriate meaning.

Импрессионизм в творчестве А. П. Чехова: история термина

The article explores the history of the term “impressionism” in reviews of Chekhov’s prose and drama, which were written by his contemporaries, and in modern research of Chekhov’s creativity (the meaning of this word in historical context). Nowadays the term “impressionism” is popular with researchers of Chekhov’s prose and drama, but there is no strict and generally accepted meaning of it. Researchers suppose various features of impressionism in literature: the impressionistic writer depicts moments of a usual daily life, uses bright and light “paints” in descriptions of nature, prefers short stories and “sketches” instead of voluminous novels and the works of such a writer resemble the “unfinished” and “slipshod” pictures of impressionists created by inconsistent brush strokes, etc. The author of the article does not deny these conceptions of impressionism but believes that the understandings of impressionism should be studied firstly in the historical way. This article studies the uses of the term “impressionism” for more than 150 years: from first readers of Chekhov’s prose and viewers of his dramas to latest researchers. The analysis of the critical articles and research papers about Chekhov created in 1890–2020 showed that the definition “an impressionist” for Chekhov’s contemporaries meant “the author who wants to impress the reader/viewer of the work by creating a certain atmosphere and does not wish to examine ethical and social issues like previous writers/artists did”; and for researches of the 20th century this word meant “the author who belongs to impressionism as a style in art”. Nowadays the term “impressionism” is ambiguous: modern researchers often use this term unsystematically in both its meanings, so it is necessary to specify what a researcher means in a particular paper.

Decodificação de Sentimentos dos Consumidores: Técnicas Avançadas de PLN para a Análise de Avaliações de Smartphones

ABSTRACT Objectives: this study aims to bridge the gap in effectively analyzing online consumer feedback on smartphones, which is often voluminous and linguistically complex. The ultimate goal is to provide smartphone manufacturers with actionable insights to refine product features and marketing strategies. We propose a dual-model framework using bidirectional encoder representations from transformers (BERT) and sentence transformers for sentiment analysis and topic modeling, respectively. This approach is intended to enhance the accuracy and depth of consumer sentiment analysis. Method: sentiment analysis and topic modeling are applied to a large dataset of smartphone reviews sourced from Kaggle and Amazon. The BERT model is used to understand the context and sentiment of words, while sentence transformers generate embeddings for clustering reviews into thematic topics. Results: our analysis revealed strong positive sentiments regarding smartphone performance and user experience, while also identifying concerns about camera and battery life. However, while the model effectively captures positive feedback, it may struggle with negative feedback and especially neutral sentiments, due to the dataset’s bias toward positive reviews. Conclusions: the application of BERT and sentence transformers provides a significant technological advancement in the field of text analysis by enhancing the granularity of sentiment detection and offering a robust framework for interpreting complex data sets. This contributes to both theoretical knowledge and practical applications in digital consumer analytics.

Rethinking Resilience: Definition, Context, and Measure

A sizeable literature treats “resilience” without defining it. Seeking to promote resilience from ubiquitous buzzword to managerially useful concept, we focus on situations in which human decision is paramount—organizational, community, and personal resilience—and exclude discussion of, e.g., resilience of fixed infrastructure and of unmanaged natural ecosystems. We argue that the word resilience, without modification or context, carries no meaning, but that strategic planning is a context that gives specific and useful meaning to resilience. Bringing in ideas from system theory, we discuss the role of anticipation, showing that resilience is not reactive; and the role of learning, showing that resilience is not a return to the status quo ante. These results provide diagnostic guidance for organizational management and for industry commentators, and a rigorous basis for further advances in resilience theory.

Development of an error correction algorithm for Kazakh language

This article discusses a method for correcting spelling errors in the Kazakh language using the advantages of morphological analysis and a model based on noisy channels. To achieve this goal, modern problems of automatic processing of Kazakh textual information were analyzed, existing linguistic resources and processing systems of the Kazakh language were systematized, the basic requirements for the development of a system for analyzing Kazakh textual information based on machine learning were determined, and models and algorithms for extracting facts from unstructured and poorly structured text arrays were developed. The search function, an enhanced spelling correction algorithm, was utilized in this work and has the ability to recommend the proper spelling of the input text. The maximum editing distance, whether to include the original word when near matches are not found, and how to handle case sensitivity and exclusion based on regular expressions are all easily adjustable features of this functionality. Because of their adaptability, algorithms can be applied to a wide range of problems, from straightforward spell checks in user interfaces to intricate natural language processing assignments. Because of the way it’s designed, the search function finds possible corrections and verifies the context of words while accounting for user preferences like verbosity and ignore markers. Most modern multilingual natural language processing programs use only the graphical stage of text processing. On the other hand, semantic text analysis or analysis of the meaning of natural language is still an important problem in the theory of artificial intelligence and computational linguistics. But in order to process the grammar and semantics of multilingual information, precreated semantic and grammatical corpora of each natural language are necessary. To solve this problem, several tasks were considered and solved. These tasks included the analysis of research in the field of machine learning methods used in the processing of textual information, the existing problems of formalization and modeling of the Kazakh language, as well as the development and implementation of models, methods and algorithms for morphological and semantic analysis of texts of the Kazakh language.

Exploring the roles of AI-Assisted ChatGPT in the field of data science

In this study, we explore the roles of AI-assisted ChatGPT (Generative Pre-trained Transformer) in the field of data science. AI-assisted ChatGPT, a powerful language model, is fine-tuned using domain-specific data for specialised data science tasks, such as sentiment analysis and named entity recognition (NER). The results reveal significant reductions in model size and memory usage with minor trade-offs in inference time, providing valuable resource-efficient deployment. Various data augmentation methods, including back-translation, synonym replacement, and contextual word embeddings, are employed to augment the training dataset. The study's results are subjected to rigorous statistical analysis, including paired t-tests and ANOVA tests, to determine the significance of the findings. The research concludes with insightful suggestions and future scope, including advanced fine-tuning strategies, model optimization techniques, and ethical considerations.

Evaluating the Impact of Text Data Augmentation on Text Classification Tasks using DistilBERT

Data augmentation entails artificially expanding the dataset's size by applying various transformations to the existing raw data. Enhancing the quality and quantity of the datasets with varying sizes by employing varieddata augmentation techniques has immense importance in the field on Natural Language Processing. Several notable applications for instance text classification, sentiment analysis, text summarization, etc. have proven to be benefitted immensely with the employment of text augmentation techniques. Hence, the paper focuses on efficient text classification using varied datasets of different sizes; small- 500 instances, medium-5564 instances and large-43934 instances.The work considers the standard DistilBERT model, a popular transformer-based language model and presents the impact on the performance of the modelafter employing different text augmentation techniques. The study specifically focuses on three augmentation methods: (a) Synonym augmentation:that involves replacing words with their synonyms to enhance vocabulary diversity and generalization, (b) Contextual word embeddings that enriches semantic understanding by leveraging pre-trained language models, and (c) Black translation that entails translating the text into a another different language and then translating it back, introducing variations in the data and capturing different linguistic patterns.Additionally,the work also discusses the combined effect of employing all three augmentation techniques simultaneously. Moreover, the study also aims compares the relation between the dataset sizes and the performance of the augmentation techniques. The study considers three standard datasets for the analysis and presents a comprehensive analysis using accuracy and F1 score as evaluation metrics. The results highlight the efficacy of each technique across small, medium, and large datasets, enabling a nuanced understanding of their benefits in different data scenarios. The findings indicate the varying degrees of improvement achieved through each augmentation technique.The enhancement achieved by applying text augmentation varied from around 2% on large datasets to 20% on smaller datasets.

Exploring Topic Coherence with PCC-LDA and BERT for Contextual Word Generation

Exploring Topic Coherence with PCC-LDA and BERT for Contextual Word Generation

Reliance and Its Significance in the Holy Qur'ān: An Objective Study

Objectives: This research aims to supplement the methods of analyzing the Qur’anic text with a new vision that opens the way for the researcher to deal with the Qur’anic word in the various contexts in which it appears in the Qur’anic text to know the common meaning of the word and its subtle connotations through each context, which gives a more accurate picture and a deeper understanding of the Qur’anic.
 Methods: This study includes the descriptive approach to the method of reading and contemplating in one of the branches of Qur’anic studies, which traces a single word with a single or multiple linguistic meanings in all contexts in which its use is mentioned in the surahs of the Holy Qur’an, collecting and studying it and completing its collection.
 Results: It has been shown that the Qur’anic use of the word is a precise and intentional use that takes into account the linguistic meaning of the word and the specific contextual meaning in which it appears. It also takes into account the meanings of the word itself in other contexts.
 Conclusions: This study included an explanation of a word from the Qur’an, which is (reclining), which indicates comfort and enjoyment, and the reward for the believers in the afterlife that God Almighty has prepared for them, as God Almighty has given many examples from the stories of the prophets, with the best example being the Prophet of God, Moses (peace be upon him), and others.

Optimized aspect and self-attention aware LSTM for target-based semantic analysis (OAS-LSTM-TSA)

PurposeAspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.Design/methodology/approachThis research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.FindingsTo detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.Originality/valueThe novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

Multilingual Learners' Strategies for Vocabulary Acquisition: Insights from Language Mixing and Borrowing

The purpose of this research is to examine the nuanced processes through which multilingual students in Saudi Arabia learn new words. In particular, it seeks to illuminate the significant role that linguistic admixture, linguistic borrowing, and linguistic competence play in this development. This study aims to add to the current literature on vocabulary learning in a bilingual or multilingual setting by investigating these factors. Fascinating insights on how these factors affect vocabulary development in a linguistically diverse setting are uncovered by the study. Three hundred people took part in in-depth tests of their ability to use Arabic and English words in context. At the same time, they were tested for their inclination toward combining and borrowing languages using carefully constructed questionnaires. Results highlight the transforming effect of lexical enrichment through linguistic borrowing and its positive correlation with vocabulary test scores. Vocabulary acquisition, especially in the target language's specific context, is also shown to be greatly aided by a high level of linguistic competence, as is made clear by the study's findings. However, a more detailed analysis is needed due to the complexity of the impact of lexical mixing. This suggests that, when used thoughtfully, blending languages may not hinder the development of one's vocabulary. The results presented here highlight the need for language education programs that are tailored to the unique linguistic needs of students and encourage the use of effective strategies for expanding students' vocabularies.

Investigating The Effects of Twitter on Students’ English Writing Skill

With a limited number of characters per tweet, writing on Twitter requires creativity. It is unique media format has not only revolutionized online communication but also created a distinct style of writing. In this case, Twitter stands as a platform that reflects the way we write and communicate in English. Researcher wants to investigate the effects of twitter on students’ English writing skill. This is a case study method. This method helps the researcher to identify the problem in detail information. The data collected from questionnaire in English writing learning process. The lecturer becomes the observer or researcher of the research. On the other hand, students become the object of the research. As the conclusion, Twitter had a significant impact on students' writing skills in English. It challenges them to express their thoughts succinctly and effectively within a limited number of characters, encouraging creativity. However, it can also lead to errors and the use of informal language. Meanwhile, Twitter provides exposure to a variety of content and interactions with people from diverse backgrounds, which improves grammatical understanding. It also helps expand vocabulary by offering different words in context.

RELATIONSHIP BETWEEN VOCABULARY ACQUISITION AND INDIVIDUAL DIFFERENCES AMONG MIDDLE SCHOOL STUDENTS

This paper explores the vocabulary acquisition of 143 middle school students from an East
 Malaysian school, examining its correlation with gender, attitudes, and perceived
 problems faced in vocabulary learning. Using the Contextualised Word Family
 (CONTEXTUALISED WORD FAMILY) model, the study tested the effectiveness of explicit
 vocabulary instruction over 30 sessions. The study utilised a one-group pretest-posttest
 design, measuring the learners’ vocabulary size through the Productive Vocabulary Levels
 Test (PVLT) and a questionnaire. The results showed an increase in vocabulary size with
 no significant difference based on gender, a very weak negative correlation with attitudes
 towards vocabulary learning, and mostly weak positive but significant relationships with
 three of the learners’ perceived problems faced in vocabulary learning. This study
 provides some important pedagogical implications for teacher practice and
 recommendations for future research.

Raw data and its implications in exegesis of Daniel 11,2b-12,3

Three kinds of raw data in Daniel 11,2b-12,3 carry crucial implications for interpretation.First, these verses comprise one discourse unit beginning with literal language. Therefore,the entire unit is basically literal. Accordingly, 11,2b-12,3 is the third angelic explanation(with elaboration) of the symbolic vision in 8,3-14, following explanations in 8,17; 19-26and 9,24-27. Second, these three parallel explanations share intratextual terminologicalpoints of contact. So contexts of words in chapters 8 and 9 reappearing in chapter 11illuminate similar contexts in chapter 11. Third, matching language of literary profilesin Daniel 11 to historical events requires accurate identification of raw historical data.Thus, verse 40 does not predict the “mortal wound” of the church of Rome inflicted byatheistic France in A.D. 1798. In verses 40-43, the religious-political church, the “king ofthe north,” defeats the religious-political “king of the south”: Islamic power.

The collocational profile of employment and work in UK employment law

The paper investigates the collocational profile of two words of similar meaning, i.e. employment and work, in the UK employment law. The study is intended to shed some light on the behaviour of the two words in the specialist language context of UK employment law. It is a corpus study based on a corpus of 12 UK employment statutes compiled for the purpose of analysing this area of English legal language. The empirical material is collected and processed with Sketch Engine, a corpus analysis tool. In particular the study looks at (a) the status of the words as terminological units and synonyms, (b) word combinations in which the words appear in the corpus, (c) the meaning that the context of use implies, and (d) the potential benefits of such empirical material in the area of foreign language for special purposes use. The findings suggest that employment and work are synonyms, yet they are used in different word combinations in the context of UK employment law which may derive from the strictly legislative context that does not welcome synonymy.

A Comparative Study of Different Dimensionality Reduction Techniques for Arabic Machine Translation

Word embeddings are widely deployed in a tremendous range of fundamental natural language processing applications and are also useful for generating representations of paragraphs, sentences, and documents. In some contexts involving constrained memory, it may be beneficial to reduce the size of word embeddings since they represent a core component of several natural language processing tasks. By reducing the dimensionality of word embeddings, their usefulness in memory-limited devices can be significantly improved, yielding gains in many real-world applications. This article aims to provide a comparative study of different dimensionality reduction techniques to generate efficient lower-dimensional word vectors. Based on empirical experiments carried out on the Arabic machine translation task, we found that the post-processing algorithm combined with independent component analysis provides optimal performance over the considered dimensionality reduction techniques. Therefore, we arrive at a new combination of the post-processing algorithm and dimensionality reduction (independent component analysis) techniques, which has not been investigated before. The latter was applied to both contextual and non-contextual word embeddings to reduce the size of the vectors while achieving a better translation quality than the original ones.

A Novel Knowledge-augmented Model Customization Approach for Arabic Offensive Language Detection

Multiple attempts to develop systems for detecting online Arabic offensive language have been explored in previous studies. However, most of these attempts do not consider the variation of Arabic dialects, cultures, and offensive phrases. In contrast, this study aims to extract knowledge from multiple offensive language datasets to build a cross-dialect and culture knowledge-based repository. This knowledge-based repository is utilized to develop novel system architecture based on customizing the AraBERT model in a unique method to preserve dialectal knowledge and offensive cultural knowledge within the contextual word embedding of BERT architecture. Performance evaluation procedures consist of statistical evaluation metrics and a behavioral checklist. Results report more effective predictions by the customized model than the uncustomized one, particularly for offensive text. The customization process allows the model to gain more knowledge of informal text in general, and a better understanding of dialectal Arabic.

APPROACHES TO AUTOMATIC WORD SENSE DISAMBIGUATION BASED ON UNEVEN DISTRIBUTION OF WORD SENSES IN CORPUS

Word Sense Disambiguation (WSD) is a key task of automatic semantic analysis that affects other upstream tasks. Nevertheless, the selection of appropriate sense of ambiguous word in context is a complicated task even for human native speakers. It is even more relevant for automatic disambiguation models. That is why we need any observations and heuristics able to make the WSD task simpler or performance higher. Researchers have noticed that the distribution of ambiguous word senses follow certain laws. In the paper we discuss three hypotheses about word senses distribution in corpus: 1) Most Frequent Sense, MFS; 2) One Sense per Discourse and 3) One Sense per Collocation. The following results were obtained on the material of a corpus of Russian texts. Most Frequent Sense based algorithm demonstrates relatively high precision on both training and test set (85.7% and 71.1% respectively). The One Sense per Discourse hypothesis has been confirmed in 93% of texts. The One Sense per Collocation hypothesis has been confirmed in 84.46% word pairs from texts. The exceptions are related to difficulties and errors by manual word sense labeling. Heuristics based on uneven distribution of ambiguous words in corpus allow to make WSD task simpler and can be used by collecting training data sets for WSD models.

Counting subwords in circular words and their Parikh matrices

The word inference problem is to determine languages such that the information on the number of occurrences of those subwords in the language can uniquely identify a word. A considerable amount of work has been done on this problem, but the same cannot be said for circular words despite growing interests on the latter due to their applications—for example, in splicing systems. Meanwhile, Parikh matrices are useful tools and well established in the study of subword occurrences. In this work, we propose two ways of counting subword occurrences in circular words. We then extend the idea of Parikh matrices to the context of circular words and investigate this extension. Motivated by the word inference problem, we study ambiguity in the identification of a circular word by its Parikh matrix. Accordingly, two rewriting rules are developed to generate ternary circular words which share the same Parikh matrix.

Personalized Query Expansion with Contextual Word Embeddings

Personalized Query Expansion, the task of expanding queries with additional terms extracted from the user-related vocabulary, is a well-known solution to improve the retrieval performance of a system w.r.t. short queries. Recent approaches rely on word embeddings to select expansion terms from user-related texts. Although promising results have been delivered with former word embedding techniques, we argue that these methods are not suited for contextual word embeddings, which produce a unique vector representation for each term occurrence. In this article, we propose a Personalized Query Expansion method designed to solve the issues arising from the use of contextual word embeddings with the current Personalized Query Expansion approaches based on word embeddings. Specifically, we employ a clustering-based procedure to identify the terms that better represent the user interests and to improve the diversity of those selected for expansion, achieving improvements of up to 4% w.r.t. the best-performing baseline in terms of MAP@100. Moreover, our approach outperforms previous ones in terms of efficiency, allowing us to achieve sub-millisecond expansion times even in data-rich scenarios. Finally, we introduce a novel metric to evaluate the expansion terms’ diversity and empirically show the unsuitability of previous approaches based on word embeddings when employed along with contextual word embeddings, which cause the selection of semantically overlapping expansion terms.