Jelena Kallas


2021

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Estonian as a Second Language Teacher’s Tools
Tiiu Üksik | Jelena Kallas | Kristina Koppel | Katrin Tsepelina | Raili Pool
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

The paper presents the results of the project “Teacher’s Tools” (et Õpetaja tööriistad) published as a subpage of the new language portal Sõnaveeb developed by the Institute of the Estonian Language. The toolbox includes four modules: vocabulary, grammar, communicative language activities and text evaluation. The tools are aimed to help teachers and specialists of Estonian as a second language plan courses, create new educational materials, exercises and tests based on CEFR level descriptions.

2020

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A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment
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
Proceedings of the Twelfth Language Resources and Evaluation Conference

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.