Unsupervised Label-aware Event Trigger and Argument Classification
- URL: http://arxiv.org/abs/2012.15243v1
- Date: Wed, 30 Dec 2020 17:47:24 GMT
- Title: Unsupervised Label-aware Event Trigger and Argument Classification
- Authors: Hongming Zhang, Haoyu Wang, Dan Roth
- Abstract summary: We propose an unsupervised event extraction pipeline, which first identifies events with available tools (e.g., SRL) and then automatically maps them to pre-defined event types.
We leverage pre-trained language models to contextually represent pre-defined types for both event triggers and arguments.
We successfully map 83% of the triggers and 54% of the arguments to the correct types, almost doubling the performance of previous zero-shot approaches.
- Score: 73.86358632937372
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Identifying events and mapping them to pre-defined event types has long been
an important natural language processing problem. Most previous work has been
heavily relying on labor-intensive and domain-specific annotations while
ignoring the semantic meaning contained in the labels of the event types. As a
result, the learned models cannot effectively generalize to new domains, where
new event types could be introduced. In this paper, we propose an unsupervised
event extraction pipeline, which first identifies events with available tools
(e.g., SRL) and then automatically maps them to pre-defined event types with
our proposed unsupervised classification model. Rather than relying on
annotated data, our model matches the semantics of identified events with those
of event type labels. Specifically, we leverage pre-trained language models to
contextually represent pre-defined types for both event triggers and arguments.
After we map identified events to the target types via representation
similarity, we use the event ontology (e.g., argument type "Victim" can only
appear as the argument of event type "Attack") as global constraints to
regularize the prediction. The proposed approach is shown to be very effective
when tested on the ACE-2005 dataset, which has 33 trigger and 22 argument
types. Without using any annotation, we successfully map 83% of the triggers
and 54% of the arguments to the correct types, almost doubling the performance
of previous zero-shot approaches.
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