Efficient discontinuous phrase-structure parsing via the generalized maximum spanning arborescence

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Abstract

We present a new method for the joint task of tagging and non-projective dependency parsing. We demonstrate its usefulness with an application to discontinuous phrase-structure parsing where decoding lexicalized spines and syntactic derivations is performed jointly. The main contributions of this paper are (1) a reduction from joint tagging and non-projective dependency parsing to the Generalized Maximum Spanning Arborescence problem, and (2) a novel decoding algorithm for this problem through Lagrangian relaxation. We evaluate this model and obtain state-of-the-art results despite strong independence assumptions.

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Corro, C., Le Roux, J., & Lacroix, M. (2017). Efficient discontinuous phrase-structure parsing via the generalized maximum spanning arborescence. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1644–1654). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1172

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