Spring 2023 Schedule
Location: Hybrid - Buchanan 126, and the zoom link below
Time: Wednesdays at 10:30am, Mountain Time
Zoom link: https://cuboulder.zoom.us/j/97014876908
Date | Title |
---|---|
01/18/23 | Cancelled, due to weather |
01/25/2023 | Planning, introductions, welcome! |
02/01/2023 | TBD |
02/08/2023 | TBD |
02/15/2023 | |
02/22/2023 | |
03/01/2023 | Diego Garcia |
03/07/2023 | Marjorie Freedman, ISI, Wikidata as an IE Ontology |
03/08/2023 | CLASIC Open House |
03/15/2023 | Role-Playing Paper-Reading: Decomposing and Recomposing Event Structure (https://tinyurl.com/p6mb7b7t) |
03/22/2023 | Ananya Ganesh: prelim |
03/29/2023 | Spring break -- no meeting! |
04/05/2023 | Kyle Gorman (invited speaker; City University of New York) |
04/12/2023 | Abteen Ebrahimi: prelim |
04/19/2023 | |
04/26/2023 | |
05/03/2023 | |
05/17/2023 | Skatje Myers, Practice Talk for Thesis Defense - Adapting Semantic Role Labeling to New Genres and Languages
Abstract: Semantic role labeling (SRL) is the identification of semantic predicates and their participants within a sentence, which is vital for deeper natural language understanding. Current SRL models require annotated text for training, but this is unavailable in many domains and languages. We explore two different ways of reducing the annotation required to produce effective SRL models: 1) using active learning to target only the most informative training instances and 2) leveraging parallel sentences to project SRL annotations from one language into the target language. |
05/18/2023 | 11am-1pm MT: Skatje Myers, REAL Thesis Defense - Adapting Semantic Role Labeling to New Genres and Languages.
Abstract: Semantic role labeling (SRL) is the identification of semantic predicates and their participants within a sentence, which is vital for deeper natural language understanding. Current SRL models require annotated text for training, but this is unavailable in many domains and languages. We explore two different ways of reducing the annotation required to produce effective SRL models: 1) using active learning to target only the most informative training instances and 2) leveraging parallel sentences to project SRL annotations from one language into the target language. |