Papers by Ranka Stanković
Using English Baits to Catch Serbian Multi-Word Terminology (L18-1)
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| Challenge: | a new method for bilingual terminology extraction is proposed for a source language and a target language. |
| Approach: | They propose to use a bilingual terminology extraction approach for a source language and a target language to extract the terminology for sri lanka. |
| Outcome: | The proposed method extracts terminology for a source language and a target language from it. |
Bridging Computational Lexicography and Corpus Linguistics: A Query Extension for OntoLex-FrAC (2024.lrec-main)
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| Challenge: | OntoLex is the dominant community standard for machine-readable lexical resources . it is currently extended with a designated module for Frequency, Attestations and Corpus-based Information . |
| Approach: | They propose a module for Frequency, Attestations and Corpus-based Information for OntoLex . the module enables RDF-based web services to exchange corpus queries dynamically . |
| Outcome: | The proposed module addresses the incorporation of corpus queries for linking dictionaries with corpus engines and enabling RDF-based web services to exchange corpus query data dynamically. |
Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection (2022.lrec-1)
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Ranka Stanković, Cvetana Krstev, Branislava Šandrih Todorović, Dusko Vitas, Mihailo Skoric, Milica Ikonić Nešić
| Challenge: | Distant reading is a new scale of description that does not displace previous scales of literary description. |
| Approach: | They present the Serbian part of the ELTeC multilingual corpus . they propose to test various methods and tools for distant reading . |
| Outcome: | The Serbian part of the ELTeC multilingual corpus is being built to test various methods and tools . Several use examples show that this sub-collection is usefull for both close and distant reading approaches. |
A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment (2020.lrec-1)
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Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Andrea Bellandi, Irene Pisani, Thomas Troelsgård, Sussi Olsen, Simon Krek, Veronika Lipp, Tamás Váradi, László Simon, András Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, José Luis Sancho, Rafael-J. Ureña-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stanković, Andrej Perdih, Dejan Gabrovsek
| Challenge: | a new dataset aims to align monolingual dictionaries with a single sense level for 15 languages . this dataset covers a wide range of languages and resources . |
| Approach: | They propose to manually align monolingual dictionaries with possible semantic relationships . they use 15 languages to create a new baseline for the task of monolingual word sense alignment . |
| Outcome: | The proposed dataset covers 15 languages and covers the more challenging task of linking general-purpose language. |
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)
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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| Challenge: | Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions. |
| Approach: | They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge. |
| Outcome: | The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian. |