A rule-based system for cross-lingual parsing of Romance languages
            with Universal Dependencies

García, Marcos; Gamallo, Pablo

doi:10.18653/v1/k17-3029

Cited by 3 publications

(1 citation statement)

References 17 publications

(20 reference statements)

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“…Our method applied a neural model on top of the grammatical restriction to achieve higher accuracy for a specific language. García et al (2017) tackled the multilingual shared task with the rule-based approach. The rules are simplified with the almost delexicalized PoS-level constraints and created with a small effort by an expert.…”

Section: Related Workmentioning

confidence: 99%

A neural parser as a direct classifier for head-final languages

Kitamura¹,

Muraoka²,

Kohita³

2018

Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

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This paper demonstrates a neural parser implementation suitable for consistently head-final languages such as Japanese. Unlike the transition-and graph-based algorithms in most state-of-the-art parsers, our parser directly selects the head word of a dependent from a limited number of candidates. This method drastically simplifies the model so that we can easily interpret the output of the neural model. Moreover, by exploiting grammatical knowledge to restrict possible modification types, we can control the output of the parser to reduce specific errors without adding annotated corpora. The neural parser performed well both on conventional Japanese corpora and the Japanese version of Universal Dependency corpus, and the advantages of distributed representations were observed in the comparison with the non-neural conventional model.

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Section: Related Workmentioning

confidence: 99%

A neural parser as a direct classifier for head-final languages

Kitamura¹,

Muraoka²,

Kohita³

2018

Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP

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Tagsets and Datasets: Some Experiments Based on Portuguese Language

Freitas

Trugo

Chalub

et al. 2018

Lecture Notes in Computer Science

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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Zeman¹,

Popel²,

Straka³

et al. 2017

Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing From Raw Text to Universal Dependencies

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399

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The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a realworld setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

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A rule-based system for cross-lingual parsing of Romance languages with Universal Dependencies

Cited by 3 publications

References 17 publications

A neural parser as a direct classifier for head-final languages

A neural parser as a direct classifier for head-final languages

Tagsets and Datasets: Some Experiments Based on Portuguese Language

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

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