Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement

Diego Alves, Božo Bekavac, Daniel Zeman, Marko Tadić


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.
Anthology ID:
2023.sigtyp-1.8
Volume:
Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Lisa Beinborn, Koustava Goswami, Saliha Muradoğlu, Alexey Sorokin, Ritesh Kumar, Andreas Shcherbakov, Edoardo M. Ponti, Ryan Cotterell, Ekaterina Vylomova
Venue:
SIGTYP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–88
Language:
URL:
https://aclanthology.org/2023.sigtyp-1.8
DOI:
10.18653/v1/2023.sigtyp-1.8
Bibkey:
Cite (ACL):
Diego Alves, Božo Bekavac, Daniel Zeman, and Marko Tadić. 2023. Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement. In Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 76–88, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Corpus-based Syntactic Typological Methods for Dependency Parsing Improvement (Alves et al., SIGTYP 2023)
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PDF:
https://aclanthology.org/2023.sigtyp-1.8.pdf
Video:
 https://aclanthology.org/2023.sigtyp-1.8.mp4