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• W3200723995 abstract "Named Entity Recognition (NER) and Relation Extraction (RE) are the core sub-tasks for information extraction. Many recent works formulate these two tasks as the span (pair) classification problem, and thus focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the dependencies between spans (pairs). In this work, we propose a novel span representation approach, named Packed Levitated Markers, to consider the dependencies between the spans (pairs) by strategically packing the markers in the encoder. In particular, we propose a group packing strategy to enable our model to process massive spans together to consider their dependencies with limited resources. Furthermore, for those more complicated span pair classification tasks, we design a subject-oriented packing strategy, which packs each subject and all its objects into an instance to model the dependencies between the same-subject span pairs. Our experiments show that our model with packed levitated markers outperforms the sequence labeling model by 0.4%-1.9% F1 on three flat NER tasks, beats the token concat model on six NER benchmarks, and obtains a 3.5%-3.6% strict relation F1 improvement with higher speed over previous SOTA models on ACE04 and ACE05. Code and models are publicly available at this https URL." @default.
• W3200723995 created "2021-09-27" @default.
• W3200723995 creator A5003413850 @default.
• W3200723995 creator A5043098453 @default.
• W3200723995 creator A5046448314 @default.
• W3200723995 date "2021-09-13" @default.
• W3200723995 modified "2023-09-27" @default.
• W3200723995 title "Pack Together: Entity and Relation Extraction with Levitated Marker." @default.
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• W3200723995 hasPublicationYear "2021" @default.
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