Learn about data annotation techniques, tools, and best practices for multilingual natural language processing in this 53-minute lecture from CMU's CS11-737 course. Explore topics such as finding and managing labelers, defining annotation tasks, interactive dictionary learning, and ensuring consistency and efficiency in the labeling process. Gain insights into popular labeling toolkits like BRAT and discover strategies for building effective multilingual NLP datasets.
Overview
Syllabus
Data Annotation
Finding Labelers
Non-native labeling
What can be labeled
Defining the task
Dictionary: Interactive Learning
Spice: Lex Learner Build Your User awb Language eng Project aug 19 Logout
Checking the Labeling
Checking for consistency
Checking for efficiency
Checking for usefulness
Choose the Task
Labeling Toolkits
BRAT Labeling Tool
Taught by
Graham Neubig
Related Courses
-
CMU Multilingual NLP 2020 - Text Classification and Sequence Labeling
-
CMU Multilingual NLP 2020 - Advanced Text Classification-Labeling
-
CMU Multilingual NLP 2020 - Multilingual Training and Cross-Lingual Transfer
-
Multilingual NLP: Words, Parts of Speech, Morphology
-
CMU Neural Nets for NLP 2020 - Multitask and Multilingual Learning
-
CMU Multilingual NLP 2020 - Data Augmentation for Machine Translation
