Idest: Learning a Distributed Representation for Event Patterns
Venue
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL'15), pp. 1140-1149
Publication Year
2015
Authors
Sebastian Krause, Enrique Alfonseca, Katja Filippova, Daniele Pighin
BibTeX
Abstract
This paper describes IDEST, a new method for learning paraphrases of event
patterns. It is based on a new neural network architecture that only relies on the
weak supervision signal that comes from the news published on the same day and
mention the same real-world entities. It can generalize across extractions from
different dates to produce a robust paraphrase model for event patterns that can
also capture meaningful representations for rare patterns. We compare it with two
state-of-the-art systems and show that it can attain comparable quality when
trained on a small dataset. Its generalization capabilities also allow it to
leverage much more data, leading to substantial quality improvements.
