28,370 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Urdu Poetry Generated by Using Deep Learning Techniques
This study provides Urdu poetry generated using different deep-learning
techniques and algorithms. The data was collected through the Rekhta website,
containing 1341 text files with several couplets. The data on poetry was not
from any specific genre or poet. Instead, it was a collection of mixed Urdu
poems and Ghazals. Different deep learning techniques, such as the model
applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU),
have been used. Natural Language Processing (NLP) may be used in machine
learning to understand, analyze, and generate a language humans may use and
understand. Much work has been done on generating poetry for different
languages using different techniques. The collection and use of data were also
different for different researchers. The primary purpose of this project is to
provide a model that generates Urdu poems by using data completely, not by
sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as
in the base paper. The results have shown good accuracy in the poems generated
by the model.Comment: 11 pages, 2 figure
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores
In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification
BEING CRITICAL IN LITERARY WORKS
Teachers should bring literary works into the class because teachers will be able to teach literacy by introducing the kinds of literary works and having some theories about literary criticisms to promote literacy development. Teachers and students go through that process together in class, step by step, from choosing literary works to sharing responses with others by encouraging students to examine their own emotions and responses as they are reading. Thus the writer talks about how all of the above components are the vehicles for expressing and transmitting students’ responses and feelings so that it can help the teachers to teach literacy. Teachers ask students to bring her or his experience, knowledge, and feelings to the novel when she or he reads, and creates a unique relationship or a unique reality and responds to it. When students as readers talk and write about the literary works, the students further the conversation, and continue the act of creation. If class members listening to students’ report are intrigued by, or angered by, or moved by the readers’ responses, these listeners may speak out, and/or read the book themselves, with the original readers’ comments in mind, thus continuing the ongoing conversation and process.Teachers should bring literary works into the class because teachers will be able to teach literacy by introducing the kinds of literary works and having some theories about literary criticisms to promote literacy development. Teachers and students go through that process together in class, step by step, from choosing literary works to sharing responses with others by encouraging students to examine their own emotions and responses as they are reading. Thus the writer talks about how all of the above components are the vehicles for expressing and transmitting students’ responses and feelings so that it can help the teachers to teach literacy. Teachers ask students to bring her or his experience, knowledge, and feelings to the novel when she or he reads, and creates a unique relationship or a unique reality and responds to it. When students as readers talk and write about the literary works, the students further the conversation, and continue the act of creation. If class members listening to students’ report are intrigued by, or angered by, or moved by the readers’ responses, these listeners may speak out, and/or read the book themselves, with the original readers’ comments in mind, thus continuing the ongoing conversation and process
Literature on Student Book and Its Effect for Developing Elementary School Teaching Materials
This research adopted the qualitative research content analysis on the 4th grade of elementary school students which aimed to know the literary material contained in the 2013 curriculum student book in Indonesia as the compulsory guidebook. The results of this research could be useful for guidelines for the development of literary materials by teachers when implementing learning planning. The findings show that students\u27 literature material is only about 30% of the Indonesian language learning material found in the 4th grade of elementary school. The lesson was limited to reading the text of the story then answering questions about the content of the text. In general, literary competence material in student books has not been emphasized on the cultivation of literary concepts. The development of literary materials in the 4th grade of elementary school should include the introduction of literary concepts, analyzing the contents of literary texts, and applying moral values in literature to be applied in daily life
Exploring figurative language recognition: a comprehensive study of human and machine approaches
Treballs Finals de Grau de Llengües i Literatures Modernes. Facultat de Filologia. Universitat de Barcelona. Curs: 2022-2023. Tutora: Elisabet Comelles Pujadas[eng] Figurative language (FL) plays a significant role in human communication. Understanding and interpreting FL is essential for humans to fully grasp the intended message, appreciate cultural nuances, and engage in effective interaction. For machines, comprehending FL presents a challenge due to its complexity and ambiguity. Enabling machines to understand FL has become increasingly important in sentiment analysis, text classification, and social media monitoring, for instance, benefits from accurately recognizing figurative expressions to capture subtle emotions and extract meaningful insights. Machine translation also requires the ability to accurately convey FL to ensure translations reflect the intended meaning and cultural nuances. Therefore, developing computational methods to enable machines to understand and interpret FL is crucial. By bridging the gap between human and machine understanding of FL, we can enhance communication, improve language-based applications, and unlock new possibilities in human-machine interactions. Keywords: figurative language, NLP, human-machine communication.[cat] El Llenguatge Figuratiu (LF) té un paper important en la comunicació humana. Per entendre completament els missatges, apreciar els matisos culturals i la interacció efectiva, és necessà ria la capacitat d'interpretar el LF. No obstant això, els ordinadors tenen dificultats per entendre la LF a causa de la seva complexitat i ambigüitat. És crÃtic que els ordinadors siguin capaços de reconèixer el LF, especialment en à rees com l'anà lisi de sentiments, la classificació de textos i la supervisió de les xarxes socials. El reconeixement precÃs del LF permet capturar emocions i extreure idees semà ntiques. La traducció automà tica també requereix una representació precisa del LF per reflectir el significat previst i els matisos culturals. Per tant, és rellevant desenvolupar mètodes computacionals que ajudin els ordinadors a comprendre i interpretar el LF. Fer un pont entre la comprensió humana i mà quina del LF pot millorar la comunicació, desenvolupar aplicacions de llenguatge i obrir noves possibilitats per a la interacció home-mà quina. Paraules clau: llenguatge figuratiu, processament del llenguatge natural, interacció home-mà quina
A prior case study of natural language processing on different domain
In the present state of digital world, computer machine do not understand the human’s ordinary language. This is the great barrier between humans and digital systems. Hence, researchers found an advanced technology that provides information to the users from the digital machine. However, natural language processing (i.e. NLP) is a branch of AI that has significant implication on the ways that computer machine and humans can interact. NLP has become an essential technology in bridging the communication gap between humans and digital data. Thus, this study provides the necessity of the NLP in the current computing world along with different approaches and their applications. It also, highlights the key challenges in the development of new NLP model
Towards a framework for socially interactive robots
250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa lÃnea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guÃa y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creÃbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo fÃsico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para asà poder mejorar su sociabilida
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