Event Representations With Tensor-Based Compositions

Authors

  • Noah Weber Stony Brook University
  • Niranjan Balasubramanian Stony Brook University
  • Nathanael Chambers United States Naval Academy

Keywords:

Event, Script, Schema

Abstract

Robust and flexible event representations are important to many core areas in language understanding. Scripts were proposed early on as a way of representing sequences of events for such understanding, and has recently attracted renewed attention. However, obtaining effective representations for modeling script-like event sequences is challenging. It requires representations that can capture event-level and scenario-level semantics. We propose a new tensor-based composition method for creating event representations. The method captures more subtle semantic interactions between an event and its entities and yields representations that are effective at multiple event-related tasks. With the continuous representations, we also devise a simple schema generation method which produces better schemas compared to a prior discrete representation based method. Our analysis shows that the tensors capture distinct usages of a predicate even when there are only subtle differences in their surface realizations.

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Published

2018-04-26

How to Cite

Weber, N., Balasubramanian, N., & Chambers, N. (2018). Event Representations With Tensor-Based Compositions. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11932

Issue

Section

Main Track: NLP and Knowledge Representation