Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem: mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, this zero-shot framework, without manual annotations, achieves performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event mentions.
|Title of host publication||ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||11|
|State||Published - 2018|
|Event||56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia|
Duration: 15 Jul 2018 → 20 Jul 2018
|Name||ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)|
|Conference||56th Annual Meeting of the Association for Computational Linguistics, ACL 2018|
|Period||15/07/18 → 20/07/18|
Bibliographical noteFunding Information:
This material is based upon work supported by United States Air Force under Contract No. FA8650-17-C-7715 and ARL NS-CTA No. W911NF-09-2-0053. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force. or the United States Government. The United States Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
© 2018 Association for Computational Linguistics