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Natural language processing expanded in the last 30 years, arising to cover a wide range of applications in various domains, among them assistive technologies, helping people with disabilities due to accidents, aging, or genetic heritance, to enhance their social integration and their daily life. The growing need in assistive techniques has led to the involvement in the usual devices of intelligent modules to improve and facilitate their use, natural language processing attaining nowadays a high applicability in this domain. The paper discusses first basic methodologies involved in natural language processing like speech recognition, speech synthesis and text processing. Some possible applications are then presented with accent on experimental realization of intelligent modules for assistive devices in the laboratory of our university.

Natural Language Processing (NLP) opens up new possibilities for arts management in practice and research. This article introduces the typical research process of NLP and presents the most important methods and techniques like Sentinent Analysis, Author Profiling, Named Entity Recognition, Topic Modeling and Trend Detection. Using recent research results and new illustrative examples, we descibe the possibilities and limitations of NLP for arts Management.

AbstractNatural Language Processing (NLP) research on AI Safety and social bias in AI has focused on safety for humans and social bias against human minorities. However, some AI ethicists have argued that the moral significance of nonhuman animals has been ignored in AI research. Therefore, the purpose of this study is to investigate whether there is speciesism, i.e., discrimination against nonhuman animals, in NLP research. First, we explain why nonhuman animals are relevant in NLP research. Next, we survey the findings of existing research on speciesism in NLP researchers, data, and models and further investigate this problem in this study. The findings of this study suggest that speciesism exists within researchers, data, and models, respectively. Specifically, our survey and experiments show that (a) among NLP researchers, even those who study social bias in AI, do not recognize speciesism or speciesist bias; (b) among NLP data, speciesist bias is inherent in the data annotated in the datasets used to evaluate NLP models; (c) OpenAI GPTs, recent NLP models, exhibit speciesist bias by default. Finally, we discuss how we can reduce speciesism in NLP research.

Because the input for Futures Studies is to a very high degree formulated as written words and texts, methods which automate the processing of texts can substantially help Futures Studies. At Shaping Tomorrow, we have developed a software system using Natural Language Processing (NLP), a subfield of Artificial Intelligence, which automatically analyzes publicly available texts and extracts future-relevant data from theses texts. This process can be used to study the futures. This article discusses this software system, explains how it works with a detailed example, and shows real-life applications and visualizations of the resulting data. The current state of this method is just the first step; a number of technological improvements and their possible benefits are explained. The implications of using this software system for the field of Futures Studies are mostly positive, but there are also a number of caveats.

Knowledge is power. Knowledge about human psychology is increasingly being produced using natural language processing (NLP) and related techniques. The power that accompanies and harnesses this knowledge should be subject to ethical controls and oversight. In this chapter, we address the ethical pitfalls that are likely to be encountered in the context of such research. These pitfalls occur at various stages of the NLP pipeline, including data acquisition, enrichment, analysis, storage, and sharing. We also address secondary uses of the results and tools developed through psychometric NLP, such as profit-driven targeted advertising, political campaigns, and domestic and international psyops. Along the way, we reflect on potential ethical guidelines and considerations that may help researchers navigate these pitfalls.