On the relation between dependency distance, crossing dependencies, and parsing

Gómez-Rodríguez, Carlos

doi:10.1016/j.plrev.2017.05.007

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In response to Liu et al (2017), Gómez-Rodríguez (2017) hypothesised that the good practical results achieved by transition-based algorithms could be because they are biased towards short dependencies. This is substantiated by Eisner and Smith (2010) who improved parser performance by imposing upper bounds on dependency length and using dependency lengths as a parsing feature.…”

Section: Dependency Distancementioning

confidence: 99%

Inherent Dependency Displacement Bias of Transition-Based Algorithms

Anderson¹,

Gómez-Rodríguez²

2020

Preprint

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A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank. There is often no discernible reason for what causes one algorithm to be more suitable for a certain treebank and less so for another. In this paper we shed some light on this by introducing the concept of an algorithm's inherent dependency displacement distribution. This characterises the bias of the algorithm in terms of dependency displacement, which quantify both distance and direction of syntactic relations. We show that the similarity of an algorithm's inherent distribution to a treebank's displacement distribution is clearly correlated to the algorithm's parsing performance on that treebank, specifically with highly significant and substantial correlations for the predominant sentence lengths in Universal Dependency treebanks. We also obtain results which show a more discrete analysis of dependency displacement does not result in any meaningful correlations.

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Section: Dependency Distancementioning

confidence: 99%

Inherent Dependency Displacement Bias of Transition-Based Algorithms

Anderson¹,

Gómez-Rodríguez²

2020

Preprint

Self Cite

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DDM at Work

Cai

Liang

Liu

2017

Physics of Life Reviews

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The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance

Anderson

Gómez-Rodríguez

2022

Computational Linguistics

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We contribute to the discussion on parsing performance in NLP by introducing a measurement that evaluates the differences between the distributions of edge displacement (the directed distance of edges) seen in training and test data. We hypothesize that this measurement will be related to differences observed in parsing performance across treebanks. We motivate this by building upon previous work and then attempt to falsify this hypothesis by using a number of statistical methods. We establish that there is a statistical correlation between this measurement and parsing performance even when controlling for potential covariants. We then use this to establish a sampling technique that gives us an adversarial and complementary split. This gives an idea of the lower and upper bounds of parsing systems for a given treebank in lieu of freshly sampled data. In a broader sense, the methodology presented here can act as a reference for future correlation-based exploratory work in NLP.

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On the relation between dependency distance, crossing dependencies, and parsing

Cited by 3 publications

References 21 publications

Inherent Dependency Displacement Bias of Transition-Based Algorithms

Inherent Dependency Displacement Bias of Transition-Based Algorithms

DDM at Work

The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance

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