Term-community-based topic detection with variable resolution

Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity. We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation. We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions. A comparison with topics detected by Latent Dirichlet Allocation is also included. Finally, we discuss factors that influence topic interpretation.Comment: 31 pages, 6 figure

Knowledge Production: Analysing Gender- and Country-Dependent Factors in Research Topics through Term Communities

Scholarly publications are among the most tangible forms of knowledge production. Therefore, it is important to analyse them, amongst other features, for gender or country differences and the incumbent inequalities. While there are many quantitative studies of publication activities and success in terms of publication numbers and citation counts, a more content-related understanding of differences in the choice of research topics is rare. The present paper suggests an innovative method of using term communities in co-occurrence networks for detecting and evaluating the gender- and country-specific distribution of topics in research publications. The method is demonstrated with a pilot study based on approximately a quarter million of publication abstracts in seven diverse research areas. In this example, the method validly reconstructs all obvious topic preferences, for instance, country-dependent language-related preferences. It also produces new insight into country-specific research focuses. It emerges that in all seven subject areas studied, topic preferences are significantly different depending on whether all authors are women, all authors are men, or there are female and male co-authors, with a tendency of male authors towards theoretical core topics, of female authors towards peripheral applied topics, and of mixed-author teams towards modern interdisciplinary topics

Combining word embeddings as a tool for subject identification

This talk shows ungoing work aiming at finding subject matter relations between text documents and word clouds. A number of increasingly successful semantic word embedding procedures - learning semantic relations from contextual distributions - have been developed in recent years. We are interested in using these embeddings for mapping documents to subjects without having to resort to supervised training of classifiers. In order to improve the quality of such a mapping it seems necessary to combine several embedding strategies. Some approaches in this direction are presented

Strategy Comparison for Semantic Zero-Shot Taxonomy Filters

In information retrieval, categorised filtering based on subject-related taxonomies is a way of supporting users in formulating their information needs in an efficient way. Progress in machine learning classification algorithms has made it possible to automatize the task of tagging or category assignment in a generally acceptable manner, provided a sufficient number of labelled example documents from all categories is put into the training process. The latter requirement, however, is a serious obstacle for a flexible use over a broad range of domains and in areas with limited amount of training data available. This contribution shows the outcome of experiments with transformer-based zero-shot text classification methods which work without any specific training. Using taxonomy descriptions, sentence aggregation with saturation, and hierarchical consistency, this approach can be enhanced to perform nearly as well as more elaborate classifiers

Overcoming losses with gain in a negative refractive index metamaterial

On the basis of a full-vectorial three-dimensional Maxwell-Bloch approach we investigate the possibility of using gain to overcome losses in a negative refractive index fishnet metamaterial. We show that appropriate placing of optically pumped laser dyes (gain) into the metamaterial structure results in a frequency band where the nonbianisotropic metamaterial becomes amplifying. In that region both the real and the imaginary part of the effective refractive index become simultaneously negative and the figure of merit diverges at two distinct frequency points.Comment: 4 pages, 4 figure

Understanding the Contribution of Direct Use of Gas to New Zealand’s Future Energy Efficiency Objectives

A report produced for the Gas Association of New Zealand. Includes 2008 Addendu

TeCoMiner: Topic Discovery Through Term Community Detection

This note is a short description of TeCoMiner, an interactive tool for exploring the topic content of text collections. Unlike other topic modeling tools, TeCoMiner is not based on some generative probabilistic model but on topological considerations about co-occurrence networks of terms. We outline the methods used for identifying topics, describe the features of the tool, and sketch an application, using a corpus of policy related scientific news on environmental issues published by the European Commission over the last decade.Comment: 8 pages, 4 figure

High-sensitivity cardiac troponin T and copeptin assays to improve diagnostic accuracy of exercise stress test in patients with suspected coronary artery disease

Background: The average diagnostic sensitivity of exercise stress tests (ESTs) is lower than that of other non-invasive cardiac stress tests. The aim of the study was to examine whether high-sensitivity cardiac troponin T (hs-cTnT) or copeptin concentrations rise in response to inducible myocardial ischaemia and may improve the diagnostic accuracy of ESTs. Methods and results: An EST was performed stepwise on a bicycle ergometer by 383 consecutive patients with suspected or progression of coronary artery disease (CAD). In addition venous blood samples for measurement of hs-cTnT and copeptin were collected prior to EST, at peak exercise, and 4 h after EST. Coronary angiography was assessed for all patients. Patients with significant CAD (n=224) were more likely to be male and older compared to patients with non-significant CAD (n=169). Positive EST was documented in 125 (55.8%) patients with significant CAD and in 69 (43.4%) patients with non-significant CAD. Copeptin and hs-cTnT concentrations at baseline were higher in patients with significant CAD (copeptin: 10.8 pmol/l (interquartile range (IQR) 8.1–15.6) vs 9.4 pmol/l (IQR 7.1–13.9); p=0.04; hs-cTnT: 3.0 ng/l (IQR <3.0–5.4) vs <3.0 ng/l (IQR <3.0); p=0.006). Hs-cTnT improved sensitivity (61.6% vs 55.8%), specificity (67.7% vs 56.6%) and the positive predictive value (PPV) (72.3% vs 64.4%) and negative (55.2% vs 47.6%) predictive value (NPV) of EST. Copeptin could not improve sensitivity (55.4% vs 55.8%) and reduced specificity, PPV and NPV. Conclusions: The measurement of hs-cTnT during EST improves sensitivity, specificity, and positive and negative predictive values. In contrast, measurement of copeptin does not improve diagnostic sensitivity and reduces specificity