@inproceedings{alves-etal-2023-corpus,
title = "Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement",
author = "Alves, Diego and
Bekavac, Bo{\v{z}}o and
Zeman, Daniel and
Tadi{\'c}, Marko",
editor = "Beinborn, Lisa and
Goswami, Koustava and
Murado{\u{g}}lu, Saliha and
Sorokin, Alexey and
Kumar, Ritesh and
Shcherbakov, Andreas and
Ponti, Edoardo M. and
Cotterell, Ryan and
Vylomova, Ekaterina",
booktitle = "Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigtyp-1.8",
doi = "10.18653/v1/2023.sigtyp-1.8",
pages = "76--88",
abstract = "This article presents a comparative analysis of four different syntactic typological approaches applied to 20 different languages to determine the most effective one to be used for the improvement of dependency parsing results via corpora combination. We evaluated these strategies by calculating the correlation between the language distances and the empirical LAS results obtained when languages were combined in pairs. From the results, it was possible to observe that the best method is based on the extraction of word order patterns which happen inside subtrees of the syntactic structure of the sentences.",
}
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<abstract>This article presents a comparative analysis of four different syntactic typological approaches applied to 20 different languages to determine the most effective one to be used for the improvement of dependency parsing results via corpora combination. We evaluated these strategies by calculating the correlation between the language distances and the empirical LAS results obtained when languages were combined in pairs. From the results, it was possible to observe that the best method is based on the extraction of word order patterns which happen inside subtrees of the syntactic structure of the sentences.</abstract>
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%0 Conference Proceedings
%T Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement
%A Alves, Diego
%A Bekavac, Božo
%A Zeman, Daniel
%A Tadić, Marko
%Y Beinborn, Lisa
%Y Goswami, Koustava
%Y Muradoğlu, Saliha
%Y Sorokin, Alexey
%Y Kumar, Ritesh
%Y Shcherbakov, Andreas
%Y Ponti, Edoardo M.
%Y Cotterell, Ryan
%Y Vylomova, Ekaterina
%S Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F alves-etal-2023-corpus
%X This article presents a comparative analysis of four different syntactic typological approaches applied to 20 different languages to determine the most effective one to be used for the improvement of dependency parsing results via corpora combination. We evaluated these strategies by calculating the correlation between the language distances and the empirical LAS results obtained when languages were combined in pairs. From the results, it was possible to observe that the best method is based on the extraction of word order patterns which happen inside subtrees of the syntactic structure of the sentences.
%R 10.18653/v1/2023.sigtyp-1.8
%U https://aclanthology.org/2023.sigtyp-1.8
%U https://doi.org/10.18653/v1/2023.sigtyp-1.8
%P 76-88
Markdown (Informal)
[Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement](https://aclanthology.org/2023.sigtyp-1.8) (Alves et al., SIGTYP 2023)
ACL