This paper aims at analyzing the changes in the fields of speech and natural language processing over the recent past 5 years (2016-2020). It is in continuation of a series of two papers that we published in 2019 on the analysis of the NLP4NLP corpus, which contained articles published in 34 major conferences and journals in the field of speech and natural language processing, over a period of 50 years (1965-2015), and analyzed with the methods developed in the field of NLP, hence its name. The extended NLP4NLP+5 corpus now covers 55 years, comprising close to 90,000 documents [+30% compared with NLP4NLP: as many articles have been published in the single year 2020 than over the first 25 years (1965-1989)], 67,000 authors (+40%), 590,000 references (+80%), and approximately 380 million words (+40%). These analyses are conducted globally or comparatively among sources and also with the general scientific literature, with a focus on the past 5 years. It concludes in identifying profound changes in research topics as well as in the emergence of a new generation of authors and the appearance of new publications around artificial intelligence, neural networks, machine learning, and word embedding.
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http://dx.doi.org/10.3389/frma.2022.863126 | DOI Listing |
J Med Internet Res
January 2025
Division of General Internal Medicine, Mayo Clinic College of Medicine and Science, 200 First St SW, Rochester, US.
Background: Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.
Objective: Show proof-of-concept that VPs powered by large language models (LLMs) generate authentic dialogs, accurate representations of patient preferences, and personalized feedback on clinical performance; and explore LLMs for rating dialog and feedback quality.
PLoS One
January 2025
Postgraduate Program in Family Health (RENASF), Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.
Introduction: Continuing Health Education is a strategy that integrates learning into the work process to transform health practices. Primary health care has proved to be a powerful space for consolidating continuing education, as it promotes reflection and learning based on the local singularities of the territory. Continuing health education is an important strategy for transforming the reality of Primary health care, reinventing work, and consequently changing practices.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Teacher Education, University of Jyväskylä, Jyväskylä, Finland.
The aim of the study was to find whether certain meaningful moments in the learning process are noticeable through features of voice and how acoustic voice analyses can be utilized in learning research. The material consisted of recordings of nine university students as they were completing tasks concerning direct electric circuits as part of their course of teacher education in physics. Prosodic features of voice-fundamental frequency (F0), sound pressure level (SPL), acoustic voice quality measured by LTAS, and pausing-were investigated.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, GC Women University Sialkot, Sialkot, Pakistan.
Modern dialogue systems rely on emotion recognition in conversation (ERC) as a core element enabling empathetic and human-like interactions. However, the weak correlation between emotions and semantics poses significant challenges to emotion recognition in dialogue. Semantically similar utterances can express different types of emotions, depending on the context or speaker.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory for Atomistic and Molecular Mechanics, Massachusetts Institute of Technology, Cambridge, MA 02139.
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges.
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