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-21270@www.clsp.jhu.edu DTSTAMP:20240328T105655Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:
Abstract
\nSocial media allows resear chers to track societal and cultural changes over time based on language a nalysis tools. Many of these tools rely on statistical algorithms which ne ed to be tuned to specific types of language. Recent studies have question ed the robustness of longitudinal analyses based on statistical methods du e to issues of temporal bias and semantic shift. To what extent are change s in semantics over time affecting the reliability of longitudinal analyse s? We examine this question through a case study: understanding shifts in mental health during the course of the COVID-19 pandemic. We demonstrate t hat a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and improv e predictive generalization over time. Ultimately\, we find that these ana lyses are critical to producing accurate longitudinal studies of social me dia.
DTSTART;TZID=America/New_York:20220207T120000 DTEND;TZID=America/New_York:20220207T131500 LOCATION:In Person or Virtual Option @ https://wse.zoom.us/j/96735183473 @ 234 Ames Hall\, 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health d uring the COVID-19 Pandemic” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media-a-case-s tudy-on-mental-health-during-the-covid-19-pandemic/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-21616@www.clsp.jhu.edu DTSTAMP:20240328T105655Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nSocial media allows resear chers to track societal and cultural changes over time based on language a nalysis tools. Many of these tools rely on statistical algorithms which ne ed to be tuned to specific types of language. Recent studies have shown th e absence of appropriate tuning\, specifically in the presence of semantic shift\, can hinder robustness of the underlying methods. However\, little is known about the practical effect this sensitivity may have on downstre am longitudinal analyses. We explore this gap in the literature through a timely case study: understanding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of s emantically-unstable features can promote significant changes in longitudi nal estimates of our target outcome. At the same time\, we demonstrate tha t a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and\, in tur n\, improve predictive generalization.
DTSTART;TZID=America/New_York:20220318T120000 DTEND;TZID=America/New_York:20220318T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Harrigian\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22374@www.clsp.jhu.edu DTSTAMP:20240328T105655Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nIn recent years\, the fiel d of Natural Language Processing has seen a profusion of tasks\, datasets\ , and systems that facilitate reasoning about real-world situations throug h language (e.g.\, RTE\, MNLI\, COMET). Such systems might\, for example\, be trained to consider a situation where “somebody dropped a glass on the floor\,” and conclude it is likely that “the glass shattered” as a result . In this talk\, I will discuss three pieces of work that revisit assumpti ons made by or about these systems. In the first work\, I develop a Defeas ible Inference task\, which enables a system to recognize when a prior ass umption it has made may no longer be true in light of new evidence it rece ives. The second work I will discuss revisits partial-input baselines\, wh ich have highlighted issues of spurious correlations in natural language r easoning datasets and led to unfavorable assumptions about models’ reasoni ng abilities. In particular\, I will discuss experiments that show models may still learn to reason in the presence of spurious dataset artifacts. F inally\, I will touch on work analyzing harmful assumptions made by reason ing models in the form of social stereotypes\, particularly in the case of free-form generative reasoning models.
\nBiography
\nRachel Rudinger is an Assistant Professor in the Department of Co mputer Science at the University of Maryland\, College Park. She holds joi nt appointments in the Department of Linguistics and the Institute for Adv anced Computer Studies (UMIACS). In 2019\, Rachel completed her Ph.D. in C omputer Science at Johns Hopkins University in the Center for Language and Speech Processing. From 2019-2020\, she was a Young Investigator at the A llen Institute for AI in Seattle\, and a visiting researcher at the Univer sity of Washington. Her research interests include computational semantics \, common-sense reasoning\, and issues of social bias and fairness in NLP.
DTSTART;TZID=America/New_York:20220916T120000 DTEND;TZID=America/New_York:20220916T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rachel Rudinger (University of Maryland\, College Park) “Not So Fas t!: Revisiting Assumptions in (and about) Natural Language Reasoning” URL:https://www.clsp.jhu.edu/events/rachel-rudinger-university-of-maryland- college-park-not-so-fast-revisiting-assumptions-in-and-about-natural-langu age-reasoning/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Rudinger\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24457@www.clsp.jhu.edu DTSTAMP:20240328T105655Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract
\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public hea lth surveillance – it is perhaps more important than ever to reflect on th e broader purpose of such systems. While much focus has been on the potent ial for this technology to improve general health outcomes\, there also ex ists a significant\, but understated\, opportunity to use this technology to address health-related disparities. Accomplishing the latter depends no t only on our ability to effectively identify addressable areas of systemi c inequality and translate them into tasks that are machine learnable\, bu t also our ability to measure\, interpret\, and counteract barriers in tra ining data that may inhibit robustness to distribution shift upon deployme nt (i.e.\, new populations\, temporal dynamics). In this talk\, we will di scuss progress made along both of these dimensions. We will begin by provi ding background on the state of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discus s their implication on prevailing views regarding language model robustnes s in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemic inequality in healthcare by identifying and c haracterizing stigmatizing language in medical records.
DTSTART;TZID=America/New_York:20240226T120000 DTEND;TZID=America/New_York:20240226T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Keith Harrigian (JHU) “Fighting Bias From Bias: Robust Natural Lang uage Processing Techniques to Promote Health Equity” URL:https://www.clsp.jhu.edu/events/keith-harrigian-jhu-fighting-bias-from- bias-robust-natural-language-processing-techniques-to-promote-health-equit y/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,February\,Harrigian END:VEVENT END:VCALENDAR