This document summarizes a lecture on word sense disambiguation and shallow semantics. It discusses word sense ambiguity and common approaches to word sense disambiguation, including knowledge-based methods like Lesk's algorithm and supervised machine learning methods. It also covers semantic analysis techniques like augmenting syntactic rules with semantic attachments and representing verb semantics using frames and semantic roles. Finally, it provides an overview of the PropBank corpus for semantic role labeling.