Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Learning to Extract Keyphrases from Text

Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)

Uso de indicadores bibliométricos para el análisis de temas emergentes y su evolución: spin-offs como caso de estudio

[ES] Las spin-offs constituyen una de las áreas de investigación más atractivas, ya que están asociadas con fenómenos como el emprendimiento, la innovación y la transferencia del conocimiento. El presente estudio muestra que la selección y el uso de indicadores bibliométricos permite identificar y caracterizar el desarrollo y la difusión de temas de investigación emergentes como el analizado. Los principales aspectos observados en relación con el desarrollo de la investigación sobre las spin-offs son que se produce un auge en el número de publicaciones después de un largo período de latencia y la marcada naturaleza multidisciplinar del área. El presente enfoque ha analizado la evolución de las publicaciones científicas, los agentes científicos involucrados en las mismas considerando diferentes niveles analíticos, las prácticas cooperativas y las características estructurales de la red de coautorías. Asimismo, se ha analizado la bibliografía citada, la evolución de los indicadores bibliométricos clave, habiéndose efectuado un análisis cualitativo de validación de contenido por parte de un experto en el campo. Se ha determinado el carácter emergente del tema a través de varios indicadores, observando que existe una superposición entre las contribuciones de los autores seminales y el momento en que se convierten en investigadores de referencia en el campo. La antigüedad de la bibliografía citada constituye un destacado indicador para establecer la naturaleza emergente del tema y monitorizar su desarrollo.[EN] Spin-offs are one of the most attractive areas of research; associated with the phenomena of entrepreneurship, innovation, and knowledge transfer. The present study shows that the selection and use of appropriate bibliometric indicators are a highly valuable method for studying emerging topics and analyzing the development and diffusion of the topic under research, including its process of emergence and growth. The primary aspects observed in relation to the development of university spin-off research includes the boom in the number of publications on the topic after a long period of latency and the pronounced multidisciplinary nature of the research. Our approach encompasses the evolution of scientific publication activity in the area, the scientific agents involved with it, and the cooperative practices and structural characteristics of the co-authorship network at different analytical levels. Also, this research explores cited literature, the evolution of key bibliometric indicators, and adds a validating qualitative analysis of content by an expert in the field. Moreover, the emergence of the topic is shown to overlap between seminal authors¿ early research contributions to the topic and the time when they become investigators of reference in the field, with their work featured among the most highly cited documents. Last but not least, the age of the cited bibliography constitutes a prominent indicator for establishing the emerging nature of a topic as well as its stage of development.This work was supported by the Conselleria de Educacion, Investigacion, Cultura y Deporte, Generalitat Valenciana (BEST 2016/153).González-Alcaide, G.; Gorraiz, J.; Hervás Oliver, JL. (2018). On the use of bibliometric indicators for the analysis of emerging topics and their evolution: spin-offs as a case study. El profesional de la información. 27(3):35-52. https://doi.org/10.3145/epi.2018.may.04S355227

Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review

Context The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with over 6000 software projects. This dataset makes it possible to estimate a project s size, effort, duration, and cost. Objective The aim of this study was to determine how and to what extent, ISBSG has been used by researchers from 2000, when the first papers were published, until June of 2012. Method A systematic mapping review was used as the research method, which was applied to over 129 papers obtained after the filtering process. Results The papers were published in 19 journals and 40 conferences. Thirty-five percent of the papers published between years 2000 and 2011 have received at least one citation in journals and only five papers have received six or more citations. Effort variable is the focus of 70.5% of the papers, 22.5% center their research in a variable different from effort and 7% do not consider any target variable. Additionally, in as many as 70.5% of papers, effort estimation is the research topic, followed by dataset properties (36.4%). The more frequent methods are Regression (61.2%), Machine Learning (35.7%), and Estimation by Analogy (22.5%). ISBSG is used as the only support in 55% of the papers while the remaining papers use complementary datasets. The ISBSG release 10 is used most frequently with 32 references. Finally, some benefits and drawbacks of the usage of ISBSG have been highlighted. Conclusion This work presents a snapshot of the existing usage of ISBSG in software development research. ISBSG offers a wealth of information regarding practices from a wide range of organizations, applications, and development types, which constitutes its main potential. However, a data preparation process is required before any analysis. Lastly, the potential of ISBSG to develop new research is also outlined.Fernández Diego, M.; González-Ladrón-De-Guevara, F. (2014). Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review. Information and Software Technology. 56(6):527-544. doi:10.1016/j.infsof.2014.01.003S52754456

Neural Related Work Summarization with a Joint Context-driven Attention Mechanism

Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.Comment: 11 pages, 3 figures, in the Proceedings of EMNLP 201

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure