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Current OPACs show their weakness in terms of ease of use and comprehension of user requests, and more generally in man/machine dialogue. Most OPAC searches are for subjects and these give the word results. Natural language processing techniques exist to reduce these difficulties. In France, natural language processing has been used to access the yellow pages (headings) of the French phone directory and the telematics services directory; examples are included. No doubt the future library systems will use these techniques to make the new OPACs really Open, Public, Accessible and Co‐operative (user‐friendly).

The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.

There is a huge amount of information and data stored in publicly available online databases that consist of large text files accessed by Boolean search techniques. It is widely held that less use is made of these databases than could or should be the case, and that one reason for this is that potential users find it difficult to identify which databases to search, to use the various command languages of the hosts and to construct the Boolean search statements required. This reasoning has stimulated a considerable amount of exploration and development work on the construction of search interfaces, to aid the inexperienced user to gain effective access to these databases. The aim of our paper is to review aspects of the design of such interfaces: to indicate the requirements that must be met if maximum aid is to be offered to the inexperienced searcher; to spell out the knowledge that must be incorporated in an interface if such aid is to be given; to describe some of the solutions that have been implemented in experimental and operational interfaces; and to discuss some of the problems encountered. The paper closes with an extensive bibliography of references relevant to online search aids, going well beyond the items explicitly mentioned in the text. An index to software appears after the bibliography at the end of the paper.

The purpose of this research paper is to explore the significance of language processing in library systems and evaluate the effectiveness of integrating artificial intelligence and generative pre-trained transformer (GPT) technology in modern libraries. Specifically, the paper focuses on SheetGPT, a Google Sheet and GPT Plugin and its impact on language processing in library systems.

This paper adopts a comprehensive analysis approach to evaluate the integration of SheetGPT in library systems. The authors outlined a user-friendly approach for installation and use of SheetGPT using its “beginner plan”, appropriate for personal/student use or extended experimentation. The study includes a quantitative analysis to provide a thorough understanding of the benefits and limitations of SheetGPT in library systems.

The findings of this research paper suggest that SheetGPT is a highly effective language-processing tool for library systems. Additionally, ChatGPT’s integration with Google Sheets and easy accessibility over Google Marketplace makes it an efficient and user-friendly tool for library professionals. Overall, this study highlights the potential of SheetGPT to enhance language processing in library systems

This research paper contributes to the existing literature by providing a comprehensive analysis of the effectiveness of SheetGPT in library systems. The study’s approach is unique in that it evaluates SheetGPT’s impact on language processing and provides insights into its benefits and limitations. The study’s findings are original and provide a valuable resource for library professionals and researchers interested in exploring the potential of SheetGPT to enhance language processing in library systems.

Text mining, natural language processing, and natural language understanding continually help businesses and organizations extract valuable insights from unstructured data. As the business environment changes, companies must integrate data from many sources to remain competitive. Text is yet another rich data source collected by an organization both internally from employees and externally from customers. The chapter begins by distinguishing and defining text mining, natural language processing, and natural language understanding. Then two case studies are presented to understand how these technologies are applied in practice, namely on human resources and customer service applications of natural language. The chapter closes with defining steps to mitigate project risk as well as exploring the many industries employing this emerging technology.

This paper introduces the topic of evaluation of natural language processing systems, and discusses the role of test suites in the linguistic evaluation of a system. The work on test suites that is being carried out within the framework of the TSNLP project is described in detail and the relevance of the project to the evaluation of machine translation systems considered.

The field of natural language processing (NLP) demonstrates rapid changes in the design of information retrieval systems and human‐computer interaction. While natural language is being looked on as the most effective tool for information retrieval in a contemporary information environment, the systems using it are only beginning to emerge. This study attempts to evaluate the current state of NLP IR systems from the user’s point of view: what techniques are used by these systems to guide their users through the search process? The analysis focused on the structure and components of the systems’ help mechanisms. Results of the study demonstrated that systems which claimed to be using natural language searching in fact used a wide range of information retrieval techniques from real natural language processing to Boolean searching. As a result, the user assistance mechanisms of these systems also varied. While pseudo‐NLP systems would suit a more traditional method of instruction, real NLP systems primarily utilised the methods of explanation and user‐system “dialogue”.

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

The paper describes techniques developed by Tome Associates to process natural language queries into search statements suitable for transmission to online text database systems. The problems discussed include word identification, the handling of unknown words, the contents and structure of system dictionaries, the use of semantic categories and classification, disambiguation of multi‐meaning words, stemming and truncation, noun compounds and indications of relationship between search terms.

The purpose of this study is to explore teen-adult dialogic interactions during the co-design of data literacy activities in order to determine the nature of teen thinking, their emotions, level of engagement, and the power of relationships between teens and adults in the context of data literacy. This study conceives of co-design as a learning space for data literacy. It investigates the teen–adult dialogic interactions and what these interactions say about the nature of teen thinking, their emotions, level of engagement and the power relationships between teens and adults.

The study conceives of co-design as a learning space for teens. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity, and Emotional Tone using transcriptions of recorded Data Labs with teens and adults. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity and Emotional Tone using transcriptions of recorded Data Labs with teens and adults.

LIWC-22 scores on the linguistic measures Analytic Thinking, Clout, Authenticity and Emotional Tone indicate that teens had a high level of friendly engagement, a relatively low sense of power compared with the adult co-designers, medium levels of spontaneity and honesty and the prevalence of positive emotions during the co-design sessions.

This study provides a concrete example of how to apply NLP in the context of data literacy in the public library, mapping the LIWC-22 findings to STEM-focused informal learning. It adds to the understanding of assessment/measurement tools and methods for designing data literacy education, stimulating further research and discussion on the ways to empower youth to engage more actively in informal learning about data.

This study applies a novel approach for exploring teen engagement within a co-design project tasked with the creation of youth-oriented data literacy activities.