Societal Impact Factors And Major Challenges For Natural Language Processing
Download Societal Impact Factors And Major Challenges For Natural Language Processing PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Societal Impact Factors And Major Challenges For Natural Language Processing book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Societal Impact Factors and Major Challenges for Natural Language Processing
Academic Paper from the year 2019 in the subject English Language and Literature Studies - Culture and Applied Geography, grade: 1,3, University of Kassel, language: English, abstract: What is math about? Basically spoken, it is about uniting separate parts and bringing together something new. To understand and to read the new creation, it is important to understand the meaning of each part to create further meaning of the whole. Where is the difference to language? To properly understand a language it is important to split it into smaller units and to understand the meaning of each unit it is necessary and helpful to understand the whole language. The mathematical formula is 1+1= language. Firstly, this seems to be confusing but lately, logical. Language does consist of smaller units which, once brought together, build up to a new system, a whole language. To understand a language it is therefore indispensable to understand logical connections but it is not necessary to be a math genius. Today’s most striking field of linguistic, Natural Language Processing (NLP), combines the ability to think logically and to analyse language in an encompassing manner. The aim of this paper is to give a brief introduction on what is Natural Language Processing (NLP) and further, to define several challenges NLP has to face due to online data bias. The challenges which concern the field of technology as well as they influence the social impact form a work frame for the overlaying field of ethical challenges in online data which are going to be displayed in this paper. Not only existing challenges but also future solutions will be a subject of discussion.
Natural Language Processing for Social Media
Author: Atefeh Farzindar
language: en
Publisher: Morgan & Claypool Publishers
Release Date: 2017-12-15
In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
Demystifying the Role of Natural Language Processing (NLP) in Mental Health
Natural Language Processing (NLP) is transforming mental health care by enabling early detection, diagnosis, and intervention for conditions such as depression, PTSD, and suicidal ideation. Advanced AI models like BioBERT and Med-BERT help analyze speech patterns, cognitive behaviors, and psychological markers to support medical practitioners in providing more effective and timely care. By leveraging deep learning and AI-driven assessments, NLP can enhance behavioral monitoring, improve treatment personalization, and optimize mental health interventions. As global mental health challenges continue to rise, integrating NLP into healthcare systems presents a crucial step toward more accessible, data-driven, and proactive mental health support. Demystifying the Role of Natural Language Processing (NLP) in Mental Health explores the latest advancements in NLP for mental health care, highlighting its role in diagnosing and treating conditions like depression, PTSD, and suicidal ideation. It examines AI-driven models, speech analysis, and behavioral monitoring techniques that enhance early intervention and personalized treatment strategies. Covering topics such as suicidal identification, emotional recognition, and patient response, this book is an excellent resource for mental health practitioners, medical practitioners, social workers, computer scientists, professionals, researchers, scholars, academicians, and more.