Semantics-based information extraction for detecting economic events

As today's financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process

All downhill from the PhD? The typical impact trajectory of US academic careers

© 2020 The Authors. Published by MIT Press. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1162/qss_a_00072.Within academia, mature researchers tend to be more senior, but do they also tend to write higher impact articles? This article assesses long-term publishing (16+ years) United States (US) researchers, contrasting them with shorter-term publishing researchers (1, 6 or 10 years). A long-term US researcher is operationalised as having a first Scopus-indexed journal article in exactly 2001 and one in 2016-2019, with US main affiliations in their first and last articles. Researchers publishing in large teams (11+ authors) were excluded. The average field and year normalised citation impact of long- and shorter-term US researchers’ journal articles decreases over time relative to the national average, with especially large falls to the last articles published that may be at least partly due to a decline in self-citations. In many cases researchers start by publishing above US average citation impact research and end by publishing below US average citation impact research. Thus, research managers should not assume that senior researchers will usually write the highest impact papers

Kriging hyperparameter tuning strategies

Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelitycomputational simulations. Of the numerous types of response surface models, kriging is perhaps one of the mosteffective, due to its ability to model complicated responses through interpolation or regression of known data whileproviding an estimate of the error in its prediction. There is, however, little information indicating the extent to whichthe hyperparameters of a kriging model need to be tuned for the resulting surrogate model to be effective. Thefollowing paper addresses this issue by investigating how often and how well it is necessary to tune thehyperparameters of a kriging model as it is updated during an optimization process. To this end, an optimizationbenchmarking procedure is introduced and used to assess the performance of five different tuning strategies over arange of problem sizes. The results of this benchmark demonstrate the performance gains that can be associated withreducing the complexity of the hyperparameter tuning process for complicated design problems. The strategy oftuning hyperparameters only once after the initial design of experiments is shown to perform poorly