Spring 2023
Spring 2023
Date | Event | Speaker | Abstract/Details |
| 01/25/2023 | Planning, introductions, welcome! | Ìý | Ìý |
| 02/22/2023 | Abhidip Bhattacharyya | Abhidip Bhattacharyya | Ìý |
| 03/01/2023 | Diego Garcia | Diego Garcia | Ìý |
| 03/07/2023 | Wikidata as an Information Extraction Ontology | Marjorie Freedman (ISI) | Ìý |
| 03/08/2023 | CLASIC Open House | Ìý | Ìý |
| 03/15/2023 | Role-Playing Paper Reading - Decomposing and Recomposing Event Structure | Ìý | Ìý |
| 03/22/2023 | Prelim | Ananya Ganesh | Ìý |
| 04/05/2023 | Invited Speaker | Kyle Gorman (City University of New York) | Ìý |
| 04/12/2023 | Prelim | Abteen Ebrahimi | Ìý |
| 04/26/2023 | Strand 1 iSAT Research - Understanding and Facilitating Collaborations | Jie Cao, Jon Cai, Ananya Ganesh, Martha Palmer | Ìý |
| 05/17/2023 | Practice Talk for Thesis Defense: Adapting Semantic Role Labeling to New Genres and Languages | Skatje Myers | 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 | Thesis Defense: Adapting Semantic Role Labeling to New Genres and Languages | Skatje Myers | 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. |