DFKI-LT - Idest: Learning a Distributed Representation for Event Patterns

Sebastian Krause, Enrique Alfonseca, Katja Filippova, Daniele Pighin
Idest: Learning a Distributed Representation for Event Patterns
1 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, The Association for Computational Linguistics, 2015
 
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.
 
Files: BibTeX, N15-1120.pdf