Question Assessment Description From the attached Word document “Research Paper”, Identify your research question. Refine it from your original question, if needed, based on the research you have completed. Create an Annotated Bibliography from the 10 references used in your Reference List assignment. Be sure to include an APA-style reference for each article. Each annotation must be 120 words in length and include the following elements: Enhancing Natural Language Processing in Virtual Assistants through Transformer Models Each reference includes an annotation that addresses the following: 1. Paraphrased summary of the article 2. Why it is considered a scholarly reference 3. Reflection on how it is applicable to your research Note on Paraphrasing: Paraphrasing the ideas of others is a requirement in academic writing and graduate study. Paraphrasing is using your own words to restate ideas or information from source material. As you write each annotation, use these three main ste
Enhancing Natural Language Processing in Virtual Assistants through Transformer Models
The research question for this annotated bibliography relates to how transformer models can improve natural language processing in virtual assistants. The paper focused on this area because of the increased utilization of virtual assistants in different fields, such as mobile data mining, IoT voice interactions, and the healthcare sector. Below is a list of ten annotated references to aid in fostering the understanding of how natural language processing can be used to enhance virtual assistants through the aid of transformer models. Our assignment writing help is at affordable prices to students of all academic levels and academic disciplines.
Annotated Bibliography
Antona, M., Margetis, G., Ntoa, S., & Degen, H. (2023). Special issue on AI in HCI. International Journal of Human-Computer Interaction, 39(9), 1723-1726. https://doi.org/10.1080/10447318.2023.2177421
This paper brings together research findings highlighting the impact of joining AI and HCI forces. It also proceeds to outline and demonstrate progress in topics related to artificial intelligence and technological solutions to address new and known problems in this vital sector of the economy. Artificial intelligence is already a discipline rich in information and is anticipated to be integrated into most aspects of everyday life. On the other hand, Human-Computer Interaction (HCI) is a field responsible for shaping the development of technology usable to more users. The sixteen papers compiled to form this publication brought together significant topics such as transparency, trust, health and well-being applications, chatbots, human-AI interaction and teaming, and responsible AI.
This article is considered a scholarly reference because it was published on 21st February 2023 as an international Taylor & Francis Group Journal, an academic database that consistently publishes reliable research. The information contained in this article was authored by Margherita Antona, George Margetis, and Stavroula Ntoa from the Foundation for Research and Technology Hellas, Greece. The fourth author of this article is Helmut Degen from Siemens Corporation, Princeton, USA. Therefore, the information in this article is accurate because scholars from the field of research and technology wrote it. This article applies to my research because it touches on vital topics such as AI-based chatbots, human-machine teaming, and Human-Robot Interaction that influence the day-to-day activities of human life.
Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7(1), 58-66. https://doi.org/10.47672/ejt.1473
Research involving natural language processing has indicated increased attention to unsupervised and semi-supervised learning techniques. These learning techniques can deduce information from annotated and non-annotated data. This article examines the various natural language processing methods, a discipline that integrates computer science, artificial intelligence, and linguistics to foster easy communication between computers and human beings. One of the major challenges that the article tries to address is enabling machines to interpret human language naturally.
This article was published on 23rd May this year. The information in this article is appropriate for my research because it gives room for examining non-annotated data, thus drawing significant insights from them. An expert in the field authored this article – Jasmin Bharatiya, who has a Ph.D. in Information Technology from the University of Cumberland. The article is reliable because it discusses natural language processing methods such as speech recognition, machine translation, morphological separation, part-of-speech tagging, and sentiment analysis. This article applies to my research because it aids in the analysis of a vast amount of data that is in unannotated form, thus increasing the accuracy of the findings.
Bouraoui, A., Jamoussi, S., & Hamadou, A. B. (2022). A comprehensive review of deep learning for natural language processing. International Journal of Data Mining, Modelling and Management, 14(2), 149-182. https://doi.org/10.1504/IJDMMM.2022.123356
Deep learning models focus on comprehending data embeddings with multiple levels of abstraction with many layers of structured or unstructured data. The primary purpose of this article is to provide an evaluation of the evolution of deep learning together with an explanation of various architec