Hasil untuk "Computational linguistics. Natural language processing"

Christopher D. Manning, M. Surdeanu, John Bauer et al.

We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of open source NLP technology. We suggest that this follows from a simple, approachable design, straightforward interfaces, the inclusion of robust and good quality analysis components, and not requiring use of a large amount of associated baggage.

Dan Jurafsky, James H. Martin

Dan Jurafsky, James H. Martin

D. Jurafsky, James H. Martin

Aneeqa Ahmad, Musarrat Azher, Muhammad Asim Mahmood

This study aims at investigating how humor is constructed in corporate memes through cognitive mechanisms and how corporate memes are used as a collective voice by Pakistani employees to express discontent with their work culture and organizational practices. Based on Conceptual blending theory (Fauconnier, & Turner, 2002), this research paper analyzes Pakistani corporate memes selected from widely used platforms such as Instagram, Facebook, Reddit, and Pinterest. Drawing upon Bakhtin’s conception of Carnivalesque (Bakhtin, 1984), the significance of workplace humor in challenging dominant narratives, questioning power imbalance and facilitating collective acknowledgement has been highlighted. The findings suggest that the dynamic cognitive processes are responsible for the successful interpretation of humor within corporate memes, depending upon integration of distinct mental spaces evoked by exposure to various ideas present in targeted memes. Further, it is revealed that the workplace memes have the potential to express critique and assert new perspectives by employing techniques such as grotesque exaggeration, visual metaphor, imagery, hyperbole and sarcasm. This article contributes to new debates on internet memes and their potential to galvanize action within digital realm.         Conflict of interest: The authors have declared no potential conflicts of interest and falsification/fabrication of data with respect to the research, authorship, and/or publication of this article.

Alina Dushkevych

The article is devoted to a comprehensive analysis of the resources of forming the English terminological system of inclusive education in the modern educational environment. The role of terminology as a tool for standardizing knowledge, communication and scientific understanding of inclusion problems is considered. It is shown that the development of inclusive education requires a clear delineation of the terminological apparatus, since it is the terms that ensure accuracy in defining concepts, unambiguousness in use and unity in the interpretation of international and national educational documents. The formation of the English-language terminological system is based on international regulatory acts, such as the "Convention on the Rights of Persons with Disabilities", "Salamanca Statement and Framework for Action on Special Needs Education", as well as numerous legislative acts of the USA (in particular the "Individuals with Disabilities Education Act" - IDEA). An important role in this process is played by glossaries, encyclopedias and textbooks on pedagogy, psychology and special education, which systematize, unify and disseminate professional vocabulary. Particular attention is paid to the analysis of key concepts of English-language inclusive education: "inclusive education", "special educational needs", "learning disabilities", "barrier-free environment", "universal design for learning", "accessibility" and their Ukrainian counterparts. It is emphasized that when translating and adapting terms, it is necessary to take into account not only the lexical-semantic aspect, but also the cultural-pedagogical context in order to avoid shifting meanings. The terminological base of inclusive education performs a number of functions: cognitive (ensuring the scientific validity of concepts), communicative (unification of interdisciplinary and intercultural communication), normative (consolidating standards in legislation and educational policy) and practical (ensuring the effective work of teachers, psychologists, social workers). It is noted that the terms must meet the criteria of accuracy, conciseness, unambiguousness and international comprehensibility.

Alessandro Lenci, Marco Passarotti, Rachele Sprugnoli et al.

Pu Jiao, Limin Ran

Abstract With the rapid development of science and technology, augmented reality technology provides intelligent and application services. The research is based on imaging techniques using augmented reality technology and camera image capture. Then, it uses screen error algorithms and scale-invariant feature transformation operators to test the quality of scene spatial models. The experimental results demonstrated that the camera significantly improved the frame rate of scene model rendering and could steadily enhance rendering efficiency. For image quality and its influencing factors, binary robust invariant scalable keypoints and scale-invariant feature transformation algorithms in viewpoint changes had the highest recall of 92%. The map drawing module, Hessian matrix, and scale-invariant feature transformation algorithm in the image blurring test achieved the highest recall rate of 98%. This demonstrates the advantage of using a scale-invariant feature transformation operator to capture scene space influence and provide a more accurate spatial model reference for augmented reality technology. This enhances the functional design of the guide system.

Tang Jinlan, Zhai Yuming, Li Lingxiao et al.

The increasing development of emerging technologies contributes to ubiquitous and personalized language learning. Hence this study reported a project of designing and applying mobile learning for a less-commonly taught language, Fijian language, at a university in China. Guided by the natural approach and the immersion method of foreign language learning, and principles of multimedia learning, a Fijian language learning application was designed and applied, involving creating a Fijian language environment via situational simulation and offering sufficient language practice and immediate feedback based on speech evaluation and adaptive learning. The rationale, course design, technology support, along with students’ Fijian learning experiences gauged from a questionnaire and an online group interview are documented and discussed. Learners reported increased interest, improvements in spoken communication and learner autonomy. The study will yield implications to future mobile language learning resources design and the pedagogy of integrating the application into teaching and learning of foreign languages.

Issiaka OUEDRAOGO, Goama NAKOULMA & Sylvie KOROGO

Résumé : La crise sécuritaire à laquelle le Burkina Faso est confronté affecte son système éducatif depuis 2016. Elle est à l’origine de la fermeture de plusieurs milliers d’établissements scolaires et du déplacement des élèves de leurs lieux habituels d’habitation vers des zones relativement mieux sécurisées. Dans ces zones d’accueil, leurs besoins éducatifs ne sont pas couverts, ce qui compromet leurs possibilités de poursuite des études. Pour accompagner l’État burkinabè dans la prise en charge de ces besoins d’urgence, des acteurs non étatiques mettent en œuvre des actions en faveur de la scolarisation des EDI. Dans le cadre d’une recherche sur les mécanismes de scolarisation de ces élèves, des enquêtes ont été réalisées dans les villes de Kaya et de Fada N’Gourma. Les résultats révèlent une multiplicité et une diversité de ces acteurs. Certains ont l’expérience de l’intervention dans le secteur humanitaire ou de l’éducation, d’autres en ont dans les deux, tandis qu’il existe des acteurs qui n’ont l’expérience dans aucun de ces domaines. Leurs actions contribuent à la scolarisation de nombreux EDI. Cependant, elles comportent des limites liées aux pratiques d’intervention et à l’insuffisance des aides apportées par rapport à la demande. Leurs interventions prennent peu en compte certains besoins prioritaires pour les EDI. Il est donc indispensable d’améliorer la gouvernance dans le domaine de l’Éducation en Situation d’Urgence (ESU) en vue d’accroître l’efficacité de ces interventions. Mots-clés : Intervention- Acteur non étatique - Scolarisation - EDI – Zone d’accueil

Om Khare, Sunil Mane, Harshmohan Kulkarni et al.

Abstract Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through computer vision techniques have become imperative for timely intervention. However, popular plant disease datasets often suffer from data imbalance, with certain classes underrepresented, hindering the performance of machine learning models. Traditional data augmentation methods, such as rotation and flipping, are limited in their effectiveness, especially when faced with imbalanced datasets. To address this limitation, we explore advanced data augmentation techniques, including Generative Adversarial Networks (GANs) such as CycleGAN and LeafGAN, which have shown promise in generating synthetic images. However, we propose an innovative approach of Object-based single Style Transfer on a single neural network for augmenting the plant disease dataset. This technique focuses on mitigating data imbalance issues within datasets, which can adversely affect the model’s ability to generalize across diverse classes. The proposed method is compared with state-of-the-art data augmentation techniques, highlighting its superiority in addressing data imbalance issues. Our approach aims to produce more realistic and diverse synthetic images, leading to improved model generalization and accuracy in plant disease classification tasks validated using different classifiers. The efficiency of our approach is validated through extensive experimentation and benchmarking against existing methods.

Maria Frasca, Davide La Torre, Gabriella Pravettoni et al.

Abstract This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box algorithms. While machine learning algorithms are increasingly employed for medical analysis and diagnosis, their complexity underscores the importance of understanding how these algorithms explain and interpret data to take informed decisions. This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field. It also provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process. Our analysis, based on 448 articles and addressing seven research questions, reveals an exponential growth in this field over the last decade. The psychological dimensions of public perception underscore the necessity for effective communication regarding the capabilities and limitations of artificial intelligence. Researchers are actively developing techniques to enhance interpretability, employing visualization methods and reducing model complexity. However, the persistent challenge lies in finding the delicate balance between achieving high performance and maintaining interpretability. Acknowledging the growing significance of artificial intelligence in aiding medical diagnosis and therapy, and the creation of interpretable artificial intelligence models is considered essential. In this dynamic context, an unwavering commitment to transparency, ethical considerations, and interdisciplinary collaboration is imperative to ensure the responsible use of artificial intelligence. This collective commitment is vital for establishing enduring trust between clinicians and patients, addressing emerging challenges, and facilitating the informed adoption of these advanced technologies in medicine.

Junichi Tsujii

This chapter provides an introduction to computational linguistics methods, with focus on their applications to the practice and study of translation. It covers computational models, methods and tools for collection, storage, indexing and analysis of linguistic data in the context of translation, and discusses the main methodological issues and challenges in this field. While an exhaustive review of existing computational linguistics methods and tools is beyond the scope of this chapter, we describe the most representative approaches, and illustrate them with descriptions of typical applications.

A. A. Kuzmina, M.A. Lifshits, V. Kostenko

The use of modern methods of computational linguistics in psychological research opens up new possibilities both for the study of personality and language and for the development of psychodiagnostics methods. This article discusses the main possible directions of such research, as well as non-obvious nuances that are important in their planning. Maximum use of the methods of computational linguistics will allow to consider the characteristics of the methods themselves, the language system, sources of texts and a sample of their authors, as well as the level of theoretical development. Each of the points will be considered in detail on the examples of studies already conducted. This review is not exhaustive but allows to create a general picture for the further search for solutions to specific research problems.