Explore the challenges and solutions in word embeddings as Felipe Bravo from Universidad de Chile presents on 'Tackling Fairness, Change, and Polysemy in Word Embeddings' for the DataLearning working group. Recorded during the weekly meeting on May 17, 2022, this 45-minute presentation delves into crucial aspects of natural language processing. Gain insights into addressing fairness issues, adapting to linguistic changes, and managing multiple meanings in word representations. Part of an interdisciplinary series featuring researchers and students developing innovative technologies in Data Assimilation and Machine Learning, this talk offers valuable knowledge for those interested in advancing language models and their applications.
Tackling Fairness, Change, and Polysemy in Word Embeddings
Overview
Syllabus
DataLearning: Tackling Fairness, Change, and Polysemy in Word Embeddings
Taught by
DataLearning@ICL
Related Courses
-
Vector Semantics and Word Embeddings in Data Science
-
CMU Advanced NLP: Bias and Fairness
-
Data Mining: Embeddings and Representations in Machine Learning - Spring 2023
-
Quantifying and Reducing Gender Stereotypes in Word Embeddings
-
Negativity and Semantic Change - Will Hamilton, Stanford University
-
Vector Semantics and Word Embeddings in Natural Language Processing
