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:20240328T144134Z 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-22400@www.clsp.jhu.edu DTSTAMP:20240328T144134Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nModern learning architectures for natural language pr ocessing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable a nd makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expressions. In this tal k\, I will give a few examples of exploring alternative architectures to t ackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and accessing knowledge i n a dedicated memory component.\nThis talk is based on several joint works with Yury Zemlyanskiy (Google Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collabo rators in Google Research.\nBiography\nFei is a research scientist at Goog le Research. Before that\, he was a Professor of Computer Science at Unive rsity of Southern California. His primary research interests are machine l earning and its application to various AI problems: speech and language pr ocessing\, computer vision\, robotics and recently weather forecast and cl imate modeling. He has a PhD (2007) from Computer and Information Scienc e from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China). DTSTART;TZID=America/New_York:20221024T120000 DTEND;TZID=America/New_York:20221024T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” URL:https://www.clsp.jhu.edu/events/fei-sha-university-of-southern-californ ia/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However\, by and large\, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expre ssions. In this talk\, I will give a few examples of exploring alternativ e architectures to tackle those challenges. In particular\, we can improve the performance of such (language) models by representing\, storing and a ccessing knowledge in a dedicated memory component.
\nThis talk is based on several joint works with Yury Zemlyanskiy (Goo gle Research)\, Michiel de Jong (USC and Google Research)\, William Cohen (Google Research and CMU) and our other collaborators in Google Research.< /p>\n
Biography
\nFei is a research scientist at Google Research. Before that\, he was a Professor of Computer Science at U niversity of Southern California. His primary research interests are machi ne learning and its application to various AI problems: speech and languag e processing\, computer vision\, robotics and recently weather forecast an d climate modeling. He has a PhD (2007) from Computer and Information Sc ience from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing\, China).
\n X-TAGS;LANGUAGE=en-US:2022\,October\,Sha END:VEVENT END:VCALENDAR