Papers with event extractors

2 papers
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)

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Challenge: Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling .
Approach: They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures .
Outcome: The proposed framework performs comparable to state-of-the-art models on three languages without annotations.
Corpus-based Open-Domain Event Type Induction (2021.emnlp-main)

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Challenge: Existing event extraction methods require predefined event types and their annotations to learn event extractors.
Approach: They propose to represent each event type as a cluster of predicate sense, object head> pairs.
Outcome: The proposed method can discover salient and high-quality event types on three datasets from different domains.

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