Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction

• URL: http://arxiv.org/abs/2107.06905v1
• Date: Wed, 14 Jul 2021 18:00:05 GMT
• Title: Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction
• Authors: Tianze Shi, Lillian Lee
• Abstract summary: We introduce a transition system and neural models for parsing bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that ours beat previous state-of-the-art approaches on the task of coordination structure prediction.
• Score: 18.71574180551552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.

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