BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21041@www.clsp.jhu.edu DTSTAMP:20240328T171336Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nNarration is a universal human practice that serves a s a key site of education\, collective memory\, fostering social belief sy stems\, and furthering human creativity. Recent studies in economics (Shil ler\, 2020)\, climate science (Bushell et al.\, 2017)\, political polariza tion (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) sugge st an emerging interdisciplinary consensus that narrative is a central con cept for understanding human behavior and beliefs. For close to half a cen tury\, the field of narratology has developed a rich set of theoretical fr ameworks for understanding narrative. And yet these theories have largely gone untested on large\, heterogenous collections of texts. Scholars conti nue to generate schemas by extrapolating from small numbers of manually ob served documents. In this talk\, I will discuss how we can use machine lea rning to develop data-driven theories of narration to better understand wh at Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?\nBiography\nAndrew Piper is Professor and William Dawson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.txtlab \n_\,\n a l aboratory for cultural analytics\, and editor of the /Journal of Cultural Analytics/\, an open-access journal dedicated to the computational study o f culture. He is the author of numerous books and articles on the relation ship of technology and reading\, including /Book Was There: Reading in Ele ctronic Times/(Chicago 2012)\, /Enumerations: Data and Literary Study/(Chi cago 2018)\, and most recently\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data/(Cambridge 2020). DTSTART;TZID=America/New_York:20211112T120000 DTEND;TZID=America/New_York:20211112T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Andrew Piper (McGill University) ” How can we use machine learning to understand narration?” URL:https://www.clsp.jhu.edu/events/andrew-piper-mcgill-university-how-can- we-use-machine-learning-to-understand-narration/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nNarration is a universal human practice that serves a s a key site of education\, collective memory\, fostering social belief sy stems\, and furthering human creativity. Recent studies in economics (Shil ler\, 2020)\, climate science (Bushell et al.\, 2017)\, political polariza tion (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) sugge st an emerging interdisciplinary consensus that narrative is a central con cept for understanding human behavior and beliefs. For close to half a cen tury\, the field of narratology has developed a rich set of theoretical fr ameworks for understanding narrative. And yet these theories have largely gone untested on large\, heterogenous collections of texts. Scholars conti nue to generate schemas by extrapolating from small numbers of manually ob served documents. In this talk\, I will discuss how we can use machine lea rning to develop data-driven theories of narration to better understand wh at Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?
\nBiography
\n< p>Andrew Piper is Professor and William D awson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.txtlab \n\na laboratory for cultural ana lytics\, and editor of the /Journal of Cultural Analytics/\, an open-acces s journal dedicated to the computational study of culture. He is the autho r of numerous books and articles on the relationship of technology and rea ding\, including /Book Was There: Reading in Electronic Times/(Chicago 201 2)\, /Enumerations: Data and Literary Study/(Chicago 2018)\, and most rece ntly\, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data /(Cambridge 2020).
\n X-TAGS;LANGUAGE=en-US:2021\,November\,Piper END:VEVENT BEGIN:VEVENT UID:ai1ec-22380@www.clsp.jhu.edu DTSTAMP:20240328T171336Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe availability of large multilingual pre-trained la nguage models has opened up exciting pathways for developing NLP technolog ies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation \, and on the challenges of handling new\, unseen-during-training language s and varieties. I will also share some of my experiences with working wit h indigenous and other endangered language communities and activists.\nBio graphy\n\nAntonios Anastasopoulos is an Assistant Professor in Computer Sc ience at George Mason University. In 2019\, Antonis received his PhD in Co mputer Science from the University of Notre Dame and then worked as a post doctoral researcher at the Language Technologies Institute at Carnegie Mel lon University. His research interests revolve around computational lingui stics and natural language processing with a focus on low-resource setting s\, endangered languages\, and cross-lingual learning.\n\n\n DTSTART;TZID=America/New_York:20220930T120000 DTEND;TZID=America/New_York:20220930T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Antonios Anastasopoulos (George Mason University) “NLP Beyond the T op-100 Languages” URL:https://www.clsp.jhu.edu/events/antonis-anastasopoulos-george-mason-uni versity/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe availability of large multilingual pre-trained la nguage models has opened up exciting pathways for developing NLP technolog ies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation \, and on the challenges of handling new\, unseen-during-training language s and varieties. I will also share some of my experiences with working wit h indigenous and other endangered language communities and activists.
\nBiography
\nAntonios Anastasopoulos is an Assistant Professor in Compu ter Science at George Mason University. In 2019\, Antonis received his PhD in Computer Science from the University of Notre Dame and then worked as a postdoctoral researcher at the Language Technologies Institute at Carneg ie Mellon University. His research interests revolve around computational linguistics and natural language processing with a focus on low-resource s ettings\, endangered languages\, and cross-lingual learning.
\n\n X-TAGS;LANGUAGE=en-US:2022\,Anastasopoulos\,September END:VEVENT END:VCALENDAR