| 1 |
One model for the learning of language.
|
|
|
|
In: Proceedings of the National Academy of Sciences of the United States of America, vol 119, iss 5 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
One model for the learning of language
|
|
|
|
In: Proc Natl Acad Sci U S A (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid
|
|
|
|
In: J Med Internet Res (2021)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource
|
|
|
|
In: J Am Med Inform Assoc (2020)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
A Light-Weight Text Summarization System for Fast Access to Medical Evidence
|
|
|
|
In: Front Digit Health (2020)
|
|
Abstract:
As the volume of published medical research continues to grow rapidly, staying up-to-date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner. Typical medical text summarization approaches require domain knowledge, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and pre-processing methods (e.g., text classification) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend, or deploy in low-resource settings, and they are often operationally slow. In this paper, we propose a fast and simple extractive summarization approach that can be easily deployed and run, and may thus aid medical experts and researchers obtain fast access to the latest research evidence. At runtime, our system utilizes similarity measurements derived from pre-trained medical domain-specific word embeddings in addition to simple features, rather than computationally-expensive pre-processing and resource-heavy knowledge bases. Automatic evaluation using ROUGE—a summary evaluation tool—on a public dataset for evidence-based medicine shows that our system's performance, despite the simple implementation, is statistically comparable with the state-of-the-art. Extrinsic manual evaluation based on recently-released COVID19 articles demonstrates that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.
|
|
Keyword:
Digital Health
|
|
URL: https://doi.org/10.3389/fdgth.2020.585559
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521877/
|
|
BASE
|
|
Hide details
|
|
|
| 6 |
Entwicklung interkultureller Handlungskompetenz : ein didaktisches Konzept für den Wirtschaftsdeutschunterricht in China am Beispiel des Einsatzes von Lernvideos
|
|
Yang, Yuan [Verfasser]. - München : Iudicium, 2019
|
|
DNB Subject Category Language
|
|
Show details
|
|
|
| 7 |
Entwicklung interkultureller Handlungskompetenz. Ein didaktisches Konzept für den Wirtschaftsdeutschunterricht in China am Beispiel des Einsatzes von Lernvideos
|
|
|
|
DNB Subject Category Language
|
|
Show details
|
|
|
| 8 |
Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages
|
|
|
|
In: Information ; Volume 11 ; Issue 1 (2019)
|
|
BASE
|
|
Show details
|
|
|
|
|
