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Who is really in control?
Published in Iryna Kuksa, Tom Fisher, Design for Personalisation, 2017
Machines can communicate with people, to offer them personal support, to help them learn, or to help change their behaviour. Natural language processing (NLP), or computational linguistics, is the sub-field of artificial intelligence devoted to building machines that use language flexibly to inform and interact with their users. This chapter sets out first to draw attention to some features of recent work on NLP – and specifically, natural language generation – that support personalised interaction between machines and humans. The focus is on personalised museum guidance, with examples drawn from our work on ILEX, the Intelligent Labelling Explorer. Then, it touches on methods that have been developed for investing NLP systems with personality. The discussion there focuses on the expression of opinions, and draws on our work on Critical Agent Dialogues. With this background in place, the chapter then explores some of the problems associated with personalisation and with personality, along with potential design solutions. Our findings show that the principal problem with personalisation is that systems can end up with too much actual autonomy; while the principal problem with personality is that systems may end up with too much perceived autonomy. The chapter concludes that there are ethical reasons why designers should develop NLP systems that can be made less (rather than more) personalised, and project less (rather than more) personality.
ŠUnderstanding Artificial Intelligence (AI)
Published in Louis J. Catania, AI for Immunology, 2021
Among the technologies of AI that make it truly more user-friendly and are having a profound effect on practical AI applications is natural language processing (NLP). NLP is a specialized software application using machine learning (ANN) and computational linguistics, enabling computers to understand and process human languages and to get computers closer to a human-level understanding of language. Recent advances in machine learning and ANNs have allowed computers to do quite a lot of useful things with natural language processing. Deep learning (CNN) has also enabled the development of programs to perform things like language translation, semantic understanding, text summarization, and chatbots.29
Text Mining: Primer, Illustration, and TXTDM Software
Published in Bruce Ratner, Statistical and Machine-Learning Data Mining, 2017
Text mining is one part of four other overlapping disciplines referred to as text processing. The disciplines include: Natural language processing (NLP) is a field of language processing by computers. Computer programs are developed to recognize human speech in various languages but mainly English.*Computational linguistics (CL) originated with efforts in the United States in the 1950s to use computers to automatically translate texts from foreign languages, particularly Russian scientific journals, into English [2]. Today, computational linguistics is advancing computer interaction with the written and spoken language to further the process of producing language in various settings of conversation between two or more people as a feature of a book, play, or movie.Information retrieval (IR) is the process of locating information resources relevant to an information request from other information assets that can be drawn on by a person or organization to function effectively. Google is perhaps the best IR search engine.Machine learning (ML) was coined by Samuel in 1959 and is the field of study that assigns computers the ability to learn without being explicitly programmed [3]. In other words, ML investigates ways in which the computer can acquire knowledge directly from data and thus learn to solve problems.
Story Analysis Using Natural Language Processing and Interactive Dashboards
Published in Journal of Computer Information Systems, 2022
NLP involves a blend of artificial intelligence, computer science, machine learning, and computational linguistics. NLP systems perform many tasks necessary for making sense of text or speech recognition. Some of these are grammatically focused, such as parts-of-speech (POS) tagging and syntactic parsing. Others are based on recognizing co-occurrences of entities in a document (coreference resolution), recognizing named entities, and interpreting temporal expressions. At a deeper level, NLP forms a venue for attempting to infer the underlying meaning of text; this has historically been termed “natural language understanding”.,12
A natural language processing framework for collecting, analyzing, and visualizing users’ sentiment on the built environment: case implementation of New York City and Seoul residences
Published in Architectural Science Review, 2022
Sun Woo Chang, Deuk Young Rhee, Han Jong Jun
NLP, also known as computational linguistics, is a technique that enables computers to learn and produce human language contents. Recent increases in computing power, developments in processing algorithms, and the availability of language big data have improved the field (Hirschberg and Manning 2015). Machine translation (Google Translate), spoken dialog systems and conversational agents (Apple’s Siri and Google Assistant), machine reading, social media mining, and an analysis of the states of the speakers are widely used NLP techniques.