Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction

Tianze Shi; Lillian Lee

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arXiv · 2107.06905

Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction

Tianze Shi·Lillian Lee

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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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Tianze Shi, Lillian Lee. 2021-07-14. Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction. https://arxiv.org/abs/2107.06905

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