Idest: Learning a Distributed Representation for Event Patterns
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.
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.
Research Areas
