Amarnag Subramanya & Partha Talukdar

Language & Computation

Week Two - 14.00-15.30 - Level: A

Room: N1


While labeled data is expensive to prepare, ever increasing amounts of unlabeled data are becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. Separately, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many speech & language processing tasks.

Recognizing this promising area of research, this course focuses on graph-based SSL algorithms (e.g., label propagation methods). At the end of this course, the attendee will walk away with the following:

  • An in-depth knowledge of graph-based SSL algorithms, and the ability to implement them.
  • The ability to decide on the suitability of graph-based SSL methods for a problem.
  • Familiarity with successful applications of graph-based SSL methods in speech and language processing.

This course may also better prepare the attendee to conduct exciting research at the intersection of speech and language and other emerging areas with natural graph-structured data (e.g., Computation Social Science). All the necessary background will be provided.