Publikation
Idest: Learning a Distributed Representation for Event Patterns
Sebastian Krause; Enrique Alfonseca; Katja Filippova; Daniele Pighin
In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL-15), May 31 - June 5, Denver, Colorado, USA, The Association for Computational Linguistics, 2015.
Zusammenfassung
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.