BEGIN:VCALENDAR VERSION:2.0 PRODID:ILLC Website X-WR-TIMEZONE:Europe/Amsterdam BEGIN:VTIMEZONE TZID:Europe/Amsterdam X-LIC-LOCATION:Europe/Amsterdam BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:19700329T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:19701025T030000 RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:/NewsandEvents/Archives/2020/newsitem/11651/11 ---17-July-2020-NASSLLI-Workshop-Natural-Logic-Mee ts-Machine-Learning-NALOMA- DTSTAMP:20200302T144422 SUMMARY:NASSLLI Workshop "Natural Logic Meets Mach ine Learning" (NALOMA) DTSTART;VALUE=DATE:20200711 DTEND;VALUE=DATE:20200717 LOCATION:Brandeis University, Waltham MA USA DESCRIPTION:NAtural LOgic Meets MAchine Learning ( NALOMA) is the first workshop of its kind, aiming to bridge the gap between Machine Learning and Nat ural Logic. It will take place from July 11-July 1 7, 2020, during the 9th North American Summer Scho ol for Logic, Language, and Information (NASSLLI) at Brandeis University in Waltham, Massachusetts. The aim of this workshop is to bring together rese archers working in both Natural Logic and Machine Learning approaches to NLI, initiating a discussio n with the two sets of researchers that have been largely unconnected up to now. We invite submissi ons on the workshop topics. Archival (long or shor t) papers should report on complete, original and unpublished research. Accepted papers will be publ ished in the workshop proceedings and will appear in the ACL anthology. See workshop web site for mo re on this. X-ALT-DESC;FMTTYPE=text/html:
NAtural L Ogic Meets MAchine Learning (NALOMA) is the first workshop of its kind, aiming to bridge the gap bet ween Machine Learning and Natural Logic. It will t ake place from July 11-July 17, 2020, during the 9 th North American Summer School for Logic, Languag e, and Information (NASSLLI) at Brandeis Universit y in Waltham, Massachusetts. The aim of this works hop is to bring together researchers working in bo th Natural Logic and Machine Learning approaches t o NLI, initiating a discussion with the two sets o f researchers that have been largely unconnected u p to now.
We invite submissio ns on the workshop topics. Archival (long or short ) papers should report on complete, original and u npublished research. Accepted papers will be publi shed in the workshop proceedings and will appear i n the ACL anthology. See workshop web site for mor e on this.