ELEXIS observer event is intended primarily for representatives of institutions that have become observers or intend to apply for observer status in ELEXIS – European Lexicographic Infrastructure. Important topics that have been covered are: Presentation of ELEXIS Horizon 2020 project (2018-2022), Presentation of (new) ELEXIS online tools and services, ELEXIS observers' role, rights and obligations, Intellectual Property Rights (IPR) issues, copyright, Lexicographic standards used and produced by ELEXIS and Project afterlife, new European infrastructure for lexicographers.
A 19-year-old man was sentenced to one year in county jail and five years of formal probation after he was convicted of participating in a street race that resulted in the death of two people, prosecutors said. Los Angeles County Superior Court Judge Gary J. Ferrari also ordered Carlos Hernandez to participate in a hospital and morgue program and attend classes that address the consequences of reckless driving, the Los Angeles County district attorney's office announced Monday. Hernandez and Elexis Garcia were street racing on Pacific Coast Highway in Wilmington on Jan. 29, 2015, when Hernandez's truck suddenly moved sideways in the direction of Garcia's lane as her car was catching up, said Deputy Dist. Brian Kang, who prosecuted the case. Garcia, 18, lost control of her car and veered into oncoming traffic, prosecutors said.
Fleetwood Mac's songs are not boring, and it's very easy to dance to them. That's what Twitter user @bottledfleet showed us all when responding to criticism from an unnamed source that "Fleetwood Mac's music is so boring, you can't even dance to it." The tweet and video went viral, with over 142,000 RTs and as a result, the song is back on Billboard's Hot Rock Songs. "Fleetwood Mac's music is so boring, you can't even dance to it" Me, an intellectual: pic.twitter.com/2QmrFycHy2 "Dreams" originally was No. 1 on the Billboard Hot 100 in June 1977.
In the data-driven future of project management, project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks. For example, many organisations require project managers to provide regular project status updates as part of the delivery assurance process. These updates typically consist of text commentary and an associated red-amber-green (RAG) status, where red indicates a failing project, amber an at-risk project and green an on-track project. Wouldn't it be great if we could automate this process, making it more consistent and objective? In this post I will describe how we can achieve exactly that by applying natural language processing (NLP) to automatically classify text commentary as either red, amber or green status.