“…NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. …”
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“…The PubMed identifiers of the scientific publications, which for the first time described the rare diseases, and the publications, which found the genes causing the diseases were added using information from OMIM, PubMed, Wikipedia, whonamedit.com, and Google Scholar. The data are available under CC0 license as spreadsheet and as RDF in a semantic model modified from DisGeNET, and was added to Wikidata. …”
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“…Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. …”
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“…The top three most often used sources of learning antibiotic use and resistance were the same among the medical and the pharmacy students; included textbooks, Wikipedia, and smart phone apps. Overall self-perceived preparedness scores showed no significant difference between pharmacy and medical students. …”
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“…Three large corpora are chosen as the subjectivity and objectivity datasets, the Shopee user review dataset (ShopeeRD) for subjectivity, together with the Wikipedia English dataset (Wiki-en) and Internet Movie Database (IMDb) for objectivity. …”
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“…Both concepts are usually applied to “the act of looking forward” (Merriam-Webster dictionary) or “the act of expecting or foreseeing something; expectation or presentiment” (Farlex free dictionary) The concept of forecasting is usually used in scientific debate to mean “to estimate or predict in advance” (American Heritage Dictionary of the English Language, 2016) or “the process of making predictions based on past and present data and most commonly by analysis of trends” (Wikipedia) (Glenn, 1994, p. 4) expressed this definition well by saying that “studying the future is not simply economic projections or sociological analysis or technological forecasting, but a multi-disciplinary examination of change in all major areas of life to find the interacting dynamics that are creating the next age.” …”
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“…This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. …”
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“…Specifically, we trained state‐of‐the‐art machine learning algorithms using contextually‐constrained text corpora (domain‐specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. …”
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“…We applied the proposed model to the Wikipedia page view data for articles on significant events in five categories: earthquakes, deaths of notable persons, aviation accidents, mass murder incidents, and terrorist attacks. …”
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“…In addition, data on the COVID-19 pandemic presented in the media (Wikipedia, TVP Info, Polsat Nes, Radio Zet) on the day preceding the completion of the survey were analyzed based on statistical analysis methods. …”
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“…As a first attempt, we generate a Czech version of the large-scale FEVER dataset built on top of Wikipedia corpus. We take a hybrid approach of machine translation and document alignment; the approach and the tools we provide can be easily applied to other languages. …”
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“…The reproducibility of the tools between two independent reviewers was evaluated and correlations between WQA score, readability statistics and Google search engine results page ranking were assessed. RESULTS: Wikipedia was the highest-ranking search result in 100% of searches performed. …”
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“…OBJECTIVE: Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy. …”
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“…Günther Schilling, University of Halle/Wittenberg, Germany, https://de.wikipedia.org/wiki/Günther_Schilling…”
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“…Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. …”
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“…The system is built upon the attention-based and recursive neural networks and introduce a piecewise representation (we divide the input sentences into three pieces to better utilize the information in the sentences), incorporates semantic information by utilizing word representations obtained from BioASQ and Wikipedia. RESULTS: The system is evaluated on the THYME corpus, a set of manually annotated clinical records from Mayo Clinic. …”
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“…METHODS: Taking European countries and Chinese provinces outside Hubei as examples, we identified 63 infector-infectee pairs from European countries from Wikipedia, and 103 infector-infectee pairs from official public sources in Chinese provinces outside Hubei. …”
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“…The mean reading grade for all articles was 12.88 (SD 1.93, CI 95% ±1.19) with significant variation between articles (P=0.001). Wikipedia was the most readable, and the FDA website was the least. …”
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“…Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. …”
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