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:20240329T082707Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNarration is a universal h uman practice that serves as a key site of education\, collective memory\, fostering social belief systems\, and furthering human creativity. Recent studies in economics (Shiller\, 2020)\, climate science (Bushell et al.\, 2017)\, political polarization (Kubin et al.\, 2021)\, and mental health (Adler et al.\, 2016) suggest an emerging interdisciplinary consensus that narrative is a central concept for understanding human behavior and belie fs. For close to half a century\, the field of narratology has developed a rich set of theoretical frameworks for understanding narrative. And yet t hese theories have largely gone untested on large\, heterogenous collectio ns of texts. Scholars continue to generate schemas by extrapolating from s mall numbers of manually observed documents. In this talk\, I will discuss how we can use machine learning to develop data-driven theories of narrat ion to better understand what Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us a pproach what we might call a minimal theory of narrativity?
\nAndrew Piper is Professor and William Dawson Scholar in the Department of Languages\, Literatures\, and Cultures at McGill University. He is the director of _.t xtlab
\n\na laboratory for cultural analytics\, and editor of the /Journal of Cultural Analytics/\, an open-access journal dedicated to the computational study of culture. He is the author of numerous books and articles on the relatio nship of technology and reading\, including /Book Was There: Reading in El ectronic Times/(Chicago 2012)\, /Enumerations: Data and Literary Study/(Ch icago 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-TAGS;LANGUAGE=en-US:2021\,November\,Piper END:VEVENT BEGIN:VEVENT UID:ai1ec-22375@www.clsp.jhu.edu DTSTAMP:20240329T082707Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nI will present our work on data augmentation using style transfer as a way to im prove domain adaptation in sequence labeling tasks. The target domain is s ocial media data\, and the task is named entity recognition (NER). The pre mise is that we can transform the labelled out of domain data into somethi ng that stylistically is more closely related to the target data. Then we can train a model on a combination of the generated data and the smaller a mount of in domain data to improve NER prediction performance. I will show recent empirical results on these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is t he scarcity of labeled data.
\nBiography
\nThamar Solorio is a Professor of Com puter Science at the University of Houston (UH). She holds graduate degree s in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include informa tion extraction from social media data\, enabling technology for code-swit ched data\, stylistic modeling of text\, and more recently multimodal appr oaches for online content understanding. She is the director and founder o f the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for he r work on authorship attribution\, and recipient of the 2014 Emerging Lead er ABIE Award in Honor of Denice Denton. She is currently serving a second term as an elected board member of the North American Chapter of the Asso ciation of Computational Linguistics and was PC co-chair for NAACL 2019. S he recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE. p> DTSTART;TZID=America/New_York:20220923T120000 DTEND;TZID=America/New_York:20220923T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Thamar Solorio (University of Houston) “Style Transfer for Data Aug mentation in Sequence Labeling Tasks” URL:https://www.clsp.jhu.edu/events/thamar-solorio-university-of-houston-st yle-transfer-for-data-augmentation-in-sequence-labeling-tasks/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT END:VCALENDAR