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• W2741237963 abstract "Temporal relation classification is becoming an active research field. Lots of methods have been proposed, while most of them focus on extracting features from external resources. Less attention has been paid to a significant advance in a closely related task: relation extraction. In this work, we borrow a state-of-the-art method in relation extraction by adopting bidirectional long short-term memory (Bi-LSTM) along dependency paths (DP). We make a “common root” assumption to extend DP representations of cross-sentence links. In the final comparison to two state-of-the-art systems on TimeBank-Dense, our model achieves comparable performance, without using external knowledge, as well as manually annotated attributes of entities (class, tense, polarity, etc.)." @default.
• W2741237963 created "2017-08-08" @default.
• W2741237963 creator A5004444958 @default.
• W2741237963 creator A5007991282 @default.
• W2741237963 date "2017-01-01" @default.
• W2741237963 modified "2023-10-12" @default.
• W2741237963 title "Classifying Temporal Relations by Bidirectional LSTM over Dependency Paths" @default.
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• W2741237963 doi "https://doi.org/10.18653/v1/p17-2001" @default.
• W2741237963 hasPublicationYear "2017" @default.
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