BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Institute for Digital Research and Education - ECPv6.15.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Institute for Digital Research and Education
X-ORIGINAL-URL:https://idre.ucla.edu
X-WR-CALDESC:Events for Institute for Digital Research and Education
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20210314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20211107T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20230312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20231105T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220225T113000
DTEND;TZID=America/Los_Angeles:20220225T123000
DTSTAMP:20260404T085830
CREATED:20220210T193439Z
LAST-MODIFIED:20220226T092741Z
UID:22725-1645788600-1645792200@idre.ucla.edu
SUMMARY:Creating a Comprehensive Lexical Resource for English Using Bayesian Deep Learning and Missing Data Methodology
DESCRIPTION:  \n \n  \nSpeaker: Bryor Snefjella\, Ph.D.\nIDRE Scholar\,\nPsychology Department\,\nUniversity of California Los Angeles \nVideo Recording: https://youtu.be/RTpW1-FtBGs \n  \nAbstract: Inquiry in the language sciences makes extensive use of open-source data sets. For example\, data sets of hand-annotations of words for properties such as their connotation and familiarity. Other common types of open-source resources include behavioural or neuroimageing recordings of responses to linguistic stimuli in controlled experiments\, or measurements taken from massive respositories of digitized natural language use. A challenge in the language sciences is extensive missing data in extant open-source data sets. Most data sets contain information on orders of magnitude fewer words than an average speaker knows\, and the words they do contain are non-randomly sampled and non-overlapping. A commonly proposed remedy to this missing data is to replace hand-annotation with machine learning. This is the approach taken by the English Lexicon Imputation Project\, the first comprehensive resource of word-level annotations created in cognitive science. In this talk I present the resource\, the Bayesian deep neural network used to create it\, and how missing data methodology was key to overcoming the limitations of prior literature on computational linguistic resource generation. The talk should be of interest to computational social scientists\, language scientists\, and those interested in deep-learning and missing data methods. \nAbout speaker: Bryor Snefjella is a postdoctoral researcher in the Psychology Department\, Cognitive Area\, mentored by Idan Blank\, Keith Holyoak\, and Hongjing Lu. Before moving to UCLA\, Bryor received a PhD in Cognitive Science of Language in McMaster University in Canada. His research on language use patterns in social media has received international media attention. Check him out on his personal website\, Twitter\, Linkedin\, and Research Gate.
URL:https://idre.ucla.edu/calendar-event/bryor-snefjella-feb25-22
CATEGORIES:Conferences and Seminars,Meetings
END:VEVENT
END:VCALENDAR