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- W4306291256 abstract "Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations. We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. In addition, we propose a Temporal Event Graph Model that predicts event instances following the temporal complex event schema. To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas. Intrinsic evaluations based on schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations. Extrinsic evaluation on schema-guided future event prediction further demonstrates the predictive power of our event graph model, significantly outperforming human schemas and baselines by more than 23.8% on HITS@1." @default.
- W4306291256 created "2022-10-15" @default.
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- W4306291256 date "2021-04-13" @default.
- W4306291256 modified "2023-10-09" @default.
- W4306291256 title "The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction" @default.
- W4306291256 doi "https://doi.org/10.48550/arxiv.2104.06344" @default.
- W4306291256 hasPublicationYear "2021" @default.
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