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Most Influential EMNLP 2018 Paper · 2026-03 edition

Multi-Task Identification Of Entities, Relations, And Coreference For Scientific Knowledge Graph Construction

Yi Luan; Luheng He; Mari Ostendorf; Hannaneh Hajishirzi

Venue
Conference on Empirical Methods in Natural Language Processing (EMNLP) 2018
Recognition
Most Influential EMNLP 2018 Paper (Rank No. 11)
Edition
2026-03
Impact factor
8
Certificate ID
020e966b18ba5709

Abstract

We introduce a multi-task setup of identifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called SciIE with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.

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