Papers by Natalia Loukachevitch
Studying Taxonomy Enrichment on Diachronic WordNet Versions (2020.coling-main)
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| Challenge: | Ontologies, taxonomies and thesauri are used in many NLP tasks but are often not maintained. |
| Approach: | They propose methods for taxonomy enrichment in a resource-poor setting . they also create novel datasets for training and evaluating taxonomies . |
| Outcome: | The proposed methods are applicable to English and Russian datasets and can be used in other languages. |
Biomedical Concept Normalization over Nested Entities with Partial UMLS Terminology in Russian (2024.lrec-main)
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| Challenge: | Existing annotations in Russian do not include all entities, but only a small fraction of them are labeled in English. |
| Approach: | They present a manually annotated PubMed abstract dataset for concept normalization in Russian. |
| Outcome: | The proposed model improves on nested named entities in a zero-shot setting on bilingual terminology. |
Corpus-based Check-up for Thesaurus (P19-1)
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| Challenge: | Existing thesaurus descriptions are expensive and time-consuming, but there are ways to maintain and improve them. |
| Approach: | They propose to apply a checking procedure to existing thesaurus to find errors . they found errors in word sense descriptions, including inaccurate relationships . |
| Outcome: | The proposed method can reveal errors in thesaurus descriptions, but it is much harder to detect them than with manual methods. |
Entity Linking over Nested Named Entities for Russian (2022.lrec-1)
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Natalia Loukachevitch, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Suresh Manandhar, Artem Shelmanov, Elena Tutubalina
| Challenge: | Entity linking is a popular NLP task, where a system needs to link a named entity to a concept in a knowledge base such as Wikidata. |
| Approach: | They describe the main design principles behind entity linking annotation in the recently released Russian NEREL dataset for information extraction. |
| Outcome: | The NEREL dataset is the largest Russian dataset annotated with entities and relations. |