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:20240328T154941Z 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-21497@www.clsp.jhu.edu DTSTAMP:20240328T154941Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nWhile the “deep learning t sunami” continues to define the state of the art in speech and language pr ocessing\, finite-state transducer grammars developed by linguists and eng ineers are still widely used in industrial\, highly-multilingual settings\ , particularly for symbolic\, “front-end” speech applications. In this tal k\, I will first briefly review the current state of the OpenFst and OpenG rm finite-state transducer libraries. I then review two “late-breaking” al gorithms found in these libraries. The first is a heuristic but highly-eff ective general-purpose optimization routine for weighted transducers. The second is an algorithm for computing the single shortest string of non-det erministic weighted acceptors which lack certain properties required by cl assic shortest-path algorithms. I will then illustrate how the OpenGrm too ls can be used to induce a finite-state string-to-string transduction mode l known as a pair n-gram model. This model has been applied to grapheme-to -phoneme conversion\, loanword detection\, abbreviation expansion\, and ba ck-transliteration\, among other tasks.
\nBiography
\nKyle Gorman is an assistant professor of linguistics at the Gradu ate Center\, City University of New York\, and director of the master’s pr ogram in computational linguistics\; he is also a software engineer in the speech and language algorithms group at Google. With Richard Sproat\, he is the coauthor of Finite-State Text Processing (Morgan & Claypool\ , 2021) and the creator of Pynini\, a finite-state text processing library for Python. He has also published on statistical methods for comparing co mputational models\, text normalization\, grapheme-to-phoneme conversion\, and morphological analysis\, as well as many topics in linguistic theory.
DTSTART;TZID=America/New_York:20220401T120000 DTEND;TZID=America/New_York:20220401T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Kyle Gorman (City University of New York) ” Weighted Finite-State T ransducers: The Later Years” URL:https://www.clsp.jhu.edu/events/kyle-gorman-city-university-of-new-york -weighted-finite-state-transducers-the-later-years/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Gorman\,March END:VEVENT END:VCALENDAR