Incorporating Constituent Syntax for Coreference Resolution
Fan Jiang, Trevor Cohn

TL;DR
This paper introduces a graph-based approach that leverages constituent syntax trees to improve coreference resolution, outperforming existing models on benchmark datasets.
Contribution
It proposes a novel method to incorporate constituent syntactic structures and higher-order neighborhood information into coreference resolution models.
Findings
Achieves state-of-the-art performance on OntoNotes 5.0 benchmark.
Effectively encodes rich hierarchical syntactic information.
Outperforms strong baseline models.
Abstract
Syntax has been shown to benefit Coreference Resolution from incorporating long-range dependencies and structured information captured by syntax trees, either in traditional statistical machine learning based systems or recently proposed neural models. However, most leading systems use only dependency trees. We argue that constituent trees also encode important information, such as explicit span-boundary signals captured by nested multi-word phrases, extra linguistic labels and hierarchical structures useful for detecting anaphora. In this work, we propose a simple yet effective graph-based method to incorporate constituent syntactic structures. Moreover, we also explore to utilise higher-order neighbourhood information to encode rich structures in constituent trees. A novel message propagation mechanism is therefore proposed to enable information flow among elements in syntax trees.…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Advanced Text Analysis Techniques
