Andrea Dömötör


Much Ado About Nothing – Identification of Zero Copulas in Hungarian Using an NMT Model
Andrea Dömötör | Zijian Győző Yang | Attila Novák
Proceedings of the Twelfth Language Resources and Evaluation Conference

The research presented in this paper concerns zero copulas in Hungarian, i.e. the phenomenon that nominal predicates lack an explicit verbal copula in the default present tense 3rd person indicative case. We created a tool based on the state-of-the-art transformer architecture implemented in Marian NMT framework that can identify and mark the location of zero copulas, i.e. the position where an overt copula would appear in the non-default cases. Our primary aim was to support quantitative corpus-based linguistic research by creating a tool that can be used to compile a corpus of significant size containing examples of nominal predicates including the location of the zero copulas. We created the training corpus for our system transforming sentences containing overt copulas into ones containing zero copula labels. However, we first needed to disambiguate occurrences of the massively ambiguous verb van ‘exist/be/have’. We performed this using a rule-base classifier relying on English translations in the English-Hungarian parallel subcorpus of the OpenSubtitles corpus. We created several NMT-based models using different sampling methods and optionally using our baseline model to synthesize additional training data. Our best model obtains almost 90% precision and 80% recall on an in-domain test set.


What does the Nom say? An algorithm for case disambiguation in Hungarian
Noémi Ligeti-Nagy | Andrea Dömötör | Noémi Vadász
Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages

Creation of a corpus with semantic role labels for Hungarian
Attila Novák | László Laki | Borbála Novák | Andrea Dömötör | Noémi Ligeti-Nagy | Ágnes Kalivoda
Proceedings of the 13th Linguistic Annotation Workshop

In this article, an ongoing research is presented, the immediate goal of which is to create a corpus annotated with semantic role labels for Hungarian that can be used to train a parser-based system capable of formulating relevant questions about the text it processes. We briefly describe the objectives of our research, our efforts at eliminating errors in the Hungarian Universal Dependencies corpus, which we use as the base of our annotation effort, at creating a Hungarian verbal argument database annotated with thematic roles, at classifying adjuncts, and at matching verbal argument frames to specific occurrences of verbs and participles in the corpus.

Syntax is clearer on the other side - Using parallel corpus to extract monolingual data
Andrea Dömötör
Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)