Andraž Repar

Also published as: Andraz Repar


2022

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Fusion of linguistic, neural and sentence-transformer features for improved term alignment
Andraz Repar | Senja Pollak | Matej Ulčar | Boshko Koloski
Proceedings of the BUCC Workshop within LREC 2022

Crosslingual terminology alignment task has many practical applications. In this work, we propose an aligning method for the shared task of the 15th Workshop on Building and Using Comparable Corpora. Our method combines several different approaches into one cohesive machine learning model, based on SVM. From shared-task specific and external sources, we crafted four types of features: cognate-based, dictionary-based, embedding-based, and combined features, which combine aspects of the other three types. We added a post-processing re-scoring method, which reducess the effect of hubness, where some terms are nearest neighbours of many other terms. We achieved the average precision score of 0.833 on the English-French training set of the shared task.

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Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources
Tamás Váradi | Bence Nyéki | Svetla Koeva | Marko Tadić | Vanja Štefanec | Maciej Ogrodniczuk | Bartłomiej Nitoń | Piotr Pęzik | Verginica Barbu Mititelu | Elena Irimia | Maria Mitrofan | Dan Tufiș | Radovan Garabík | Simon Krek | Andraž Repar
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This article presents the current outcomes of the CURLICAT CEF Telecom project, which aims to collect and deeply annotate a set of large corpora from selected domains. The CURLICAT corpus includes 7 monolingual corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing selected samples from respective national corpora. These corpora are automatically tokenized, lemmatized and morphologically analysed and the named entities annotated. The annotations are uniformly provided for each language specific corpus while the common metadata schema is harmonised across the languages. Additionally, the corpora are annotated for IATE terms in all languages. The file format is CoNLL-U Plus format, containing the ten columns specific to the CoNLL-U format and three extra columns specific to our corpora as defined by Varádi et al. (2020). The CURLICAT corpora represent a rich and valuable source not just for training NMT models, but also for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.

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Curated Multilingual Language Resources for CEF AT (CURLICAT): overall view
Tamás Váradi | Marko Tadić | Svetla Koeva | Maciej Ogrodniczuk | Dan Tufiş | Radovan Garabík | Simon Krek | Andraž Repar
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

The work in progress on the CEF Action CURLICA T is presented. The general aim of the Action is to compile curated datasets in seven languages of the con- sortium in domains of relevance to Euro- pean Digital Service Infrastructures (DSIs) in order to enhance the eTransla- tion services.

2021

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Aligning Estonian and Russian news industry keywords with the help of subtitle translations and an environmental thesaurus
Andraž Repar | Andrej Shumakov
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

This paper presents the implementation of a bilingual term alignment approach developed by Repar et al. (2019) to a dataset of unaligned Estonian and Russian keywords which were manually assigned by journalists to describe the article topic. We started by separating the dataset into Estonian and Russian tags based on whether they are written in the Latin or Cyrillic script. Then we selected the available language-specific resources necessary for the alignment system to work. Despite the domains of the language-specific resources (subtitles and environment) not matching the domain of the dataset (news articles), we were able to achieve respectable results with manual evaluation indicating that almost 3/4 of the aligned keyword pairs are at least partial matches.

2020

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Gigafida 2.0: The Reference Corpus of Written Standard Slovene
Simon Krek | Špela Arhar Holdt | Tomaž Erjavec | Jaka Čibej | Andraz Repar | Polona Gantar | Nikola Ljubešić | Iztok Kosem | Kaja Dobrovoljc
Proceedings of the Twelfth Language Resources and Evaluation Conference

We describe a new version of the Gigafida reference corpus of Slovene. In addition to updating the corpus with new material and annotating it with better tools, the focus of the upgrade was also on its transformation from a general reference corpus, which contains all language variants including non-standard language, to the corpus of standard (written) Slovene. This decision could be implemented as new corpora dedicated specifically to non-standard language emerged recently. In the new version, the whole Gigafida corpus was deduplicated for the first time, which facilitates automatic extraction of data for the purposes of compilation of new lexicographic resources such as the collocations dictionary and the thesaurus of Slovene.

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The MARCELL Legislative Corpus
Tamás Váradi | Svetla Koeva | Martin Yamalov | Marko Tadić | Bálint Sass | Bartłomiej Nitoń | Maciej Ogrodniczuk | Piotr Pęzik | Verginica Barbu Mititelu | Radu Ion | Elena Irimia | Maria Mitrofan | Vasile Păiș | Dan Tufiș | Radovan Garabík | Simon Krek | Andraz Repar | Matjaž Rihtar | Janez Brank
Proceedings of the Twelfth Language Resources and Evaluation Conference

This article presents the current outcomes of the MARCELL CEF Telecom project aiming to collect and deeply annotate a large comparable corpus of legal documents. The MARCELL corpus includes 7 monolingual sub-corpora (Bulgarian, Croatian, Hungarian, Polish, Romanian, Slovak and Slovenian) containing the total body of respective national legislative documents. These sub-corpora are automatically sentence split, tokenized, lemmatized and morphologically and syntactically annotated. The monolingual sub-corpora are complemented by a thematically related parallel corpus (Croatian-English). The metadata and the annotations are uniformly provided for each language specific sub-corpus. Besides the standard morphosyntactic analysis plus named entity and dependency annotation, the corpus is enriched with the IATE and EUROVOC labels. The file format is CoNLL-U Plus Format, containing the ten columns specific to the CoNLL-U format and four extra columns specific to our corpora. The MARCELL corpora represents a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification.