Ivaylo Radev


2021

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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Jakub Piskorski | Bogdan Babych | Zara Kancheva | Olga Kanishcheva | Maria Lebedeva | Michał Marcińczuk | Preslav Nakov | Petya Osenova | Lidia Pivovarova | Senja Pollak | Pavel Přibáň | Ivaylo Radev | Marko Robnik-Sikonja | Vasyl Starko | Josef Steinberger | Roman Yangarber
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2021 Conference. Ten teams participated in the competition. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task. Detailed valuation information is available on the shared task web page.

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Monitoring Fact Preservation, Grammatical Consistency and Ethical Behavior of Abstractive Summarization Neural Models
Iva Marinova | Yolina Petrova | Milena Slavcheva | Petya Osenova | Ivaylo Radev | Kiril Simov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

The paper describes a system for automatic summarization in English language of online news data that come from different non-English languages. The system is designed to be used in production environment for media monitoring. Automatic summarization can be very helpful in this domain when applied as a helper tool for journalists so that they can review just the important information from the news channels. However, like every software solution, the automatic summarization needs performance monitoring and assured safe environment for the clients. In media monitoring environment the most problematic features to be addressed are: the copyright issues, the factual consistency, the style of the text and the ethical norms in journalism. Thus, the main contribution of our present work is that the above mentioned characteristics are successfully monitored in neural automatic summarization models and improved with the help of validation, fact-preserving and fact-checking procedures.

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Handling synset overgeneration: Sense Merging in BTB-WN
Ivaylo Radev | Zara Kancheva
Proceedings of the Student Research Workshop Associated with RANLP 2021

The paper reports on an effort to reconsider the representation of some cases of derivational paradigm patterns in Bulgarian. The new treatment implemented within BulTreeBank-WordNet (BTB-WN), a wordnet for Bulgarian, is the grouping together of related words that have a common main meaning in the same synset while the nuances in sense are to be encoded within the synset as a modification functions over the main meaning. In this way, we can solve the following challenges: (1) to avoid the influence of English Wordnet (EWN) synset distinctions over Bulgarian that was a result from the translation of some of the synsets from Core WordNet; (2) to represent the common meaning of such derivation patterns just once and to improve the management of BTB-WN, and (3) to encode idiosyncratic usages locally to the corresponding synsets instead of introducing new semantic relations.

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 12th 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.

2019

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Adding Linguistic Knowledge to NLP Tasks for Bulgarian: The Verb Paradigm Patterns
Ivaylo Radev
Proceedings of the Student Research Workshop Associated with RANLP 2019

This paper discusses some possible usages of one unexplored lexical language resource containing Bulgarian verb paradigms and their English translations. This type of data can be used for machine translation, generation of pseudo corpora/language exercises, and evaluation of parsers. Upon completion, the resource will be linked with other existing resources such as the morphological lexicon, valency lexicon, as well as BTB-WordNet.