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:20240329T150502Z 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-22395@www.clsp.jhu.edu DTSTAMP:20240329T150502Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRecursive calls over recursive data are widely useful for generating probability distributions\, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful\, but unfortunately\, ex isting probabilistic programming languages do not perform exact inference on recursive calls over recursive data\, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wi de variety of recursion can be expressed naturally\, and inference carried out exactly. For instance\, probabilistic pushdown automata and their gen eralizations are easy to express\, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types usin g program transformations related to defunctionalization and refunctionali zation. These transformations are assured correct by a linear type system\ , and a successful choice of transformations\, if there is one\, is guaran teed to be found by a greedy algorithm. I will also describe the implement ation of this language in two phases: first\, compilation to a factor grap h grammar\, and second\, computing the sum-product of the factor graph gra mmar.\n\nBiography\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an associate professor in the Department of Computer Science and Engin eering at the University of Notre Dame. His research is on computational m odels for learning human languages\, particularly how to translate from on e language to another. His work on applying formal grammars and machine le arning to translation has been recognized with two best paper awards (at A CL 2005 and NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google\, and Amazon\, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR\, and is curren tly on the editorial board of Transactions of the ACL. DTSTART;TZID=America/New_York:20221017T120000 DTEND;TZID=America/New_York:20221017T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre Dame) and Chung-chieh Shan (Indiana)” URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
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