Ryszard Tuora


2024

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Polish Discourse Corpus (PDC): Corpus Design, ISO-Compliant Annotation, Data Highlights, and Parser Development
Maciej Ogrodniczuk | Aleksandra Tomaszewska | Daniel Ziembicki | Sebastian Żurowski | Ryszard Tuora | Aleksandra Zwierzchowska
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper presents the Polish Discourse Corpus, a pioneering resource of this kind for Polish and the first corpus in Poland to employ the ISO standard for discourse relation annotation. The Polish Discourse Corpus adopts ISO 24617-8, a segment of the Language Resource Management – Semantic Annotation Framework (SemAF), which outlines a set of core discourse relations adaptable for diverse languages and genres. The paper overviews the corpus architecture, annotation procedures, the challenges that the annotators have encountered, as well as key statistical data concerning discourse relations and connectives in the corpus. It further discusses the initial phases of the discourse parser tailored for the ISO 24617-8 framework. Evaluations on the efficacy and potential refinement areas of the corpus annotation and parsing strategies are also presented. The final part of the paper touches upon anticipated research plans to improve discourse analysis techniques in the project and to conduct discourse studies involving multiple languages.

2021

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Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)
Ryszard Tuora | Adam Przepiórkowski | Aleksander Leczkowski
Findings of the Association for Computational Linguistics: EMNLP 2021

This paper contributes to the thread of research on the learnability of different dependency annotation schemes: one (‘semantic’) favouring content words as heads of dependency relations and the other (‘syntactic’) favouring syntactic heads. Several studies have lent support to the idea that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers. This may be explained by postulating that syntactic approaches are generally more learnable. In this study, we test this hypothesis by comparing the performance of five parsing systems (both transition- and graph-based) on a selection of 21 treebanks, each in a ‘semantic’ variant, represented by standard UD (Universal Dependencies), and a ‘syntactic’ variant, represented by SUD (Surface-syntactic Universal Dependencies): unlike previously reported experiments, which considered learnability of ‘semantic’ and ‘syntactic’ annotations of particular constructions in vitro, the experiments reported here consider whole annotation schemes in vivo. Additionally, we compare these annotation schemes using a range of quantitative syntactic properties, which may also reflect their learnability. The results of the experiments show that SUD tends to be more learnable than UD, but the advantage of one or the other scheme depends on the parser and the corpus in question.