Lung-Hao Lee, Liang-Chih Yu, Natalia V Loukachevitch, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Mohammad. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

We present RuSemCor, an open Word Sense Disambiguation (WSD) corpus for Russian. The corpus was constructed by manually linking tokens from the OpenCorpora corpus to senses in the Russian wordnet RuWordNet. It consists of 869 documents with 121,710 tokens of which 51,588 are wordnet annotated. The resource is represented using the NIF, OLiA, OntoLex, and Global WordNet ontologies and integrated into the Linguistic Linked Open Data cloud. We used RuSemCor as a diagnostic benchmark to evaluate a range of WSD methods. Our experiments yielded three main findings. 1)~Generative LLMs substantially outperform traditional knowledge-based methods such as Personalized PageRank. 2)~Despite their strengths, generative LLMs do not surpass encoder-based models specifically trained for WSD. 3)~Incorporating lexical-semantic relations from RuWordNet produces mixed results: it enhances the performance of encoder-based models and leading LLMs like GPT-4, DeepSeek, and Mistral 24B, but tends to degrade accuracy for smaller generative models such as GPT-3 and Mistral 7B. The resource is distributed under the CC BY-SA open license and is available at: https://github.com/LLOD-Ru/rusemcor.

We present a new manually annotated dataset of PubMed abstracts for concept normalization in Russian. It contains over 23,641 entity mentions in 756 documents linked to 4,544 unique concepts from the UMLS ontology. Compared to existing corpora, we explore two novel annotation characteristics: the nestedness of named entities and the incompleteness of the Russian medical terminology in UMLS. 4,424 entity mentions are linked to 1,535 unique English concepts absent in the Russian part of the UMLS ontology. We present several baselines for normalization over nested named entities obtained with state-of-the-art models such as SapBERT. Our experimental results show that models pre-trained on graph structural data from UMLS achieve superior performance in a zero-shot setting on bilingual terminology.

MOTIVATION: This article describes NEREL-BIO-an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL by introducing domain-specific entity types. NEREL-BIO annotation scheme covers both general and biomedical domains making it suitable for domain transfer experiments. NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL. Nested named entities may cross entity boundaries to connect to shorter entities nested within longer entities, making them harder to detect. RESULTS: NEREL-BIO contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. Thus, NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL → NEREL-BIO) and cross-language (English → Russian) transfer. We experiment with both transformer-based sequence models and machine reading comprehension models and report their results. AVAILABILITY AND IMPLEMENTATION: The dataset and annotation guidelines are freely available at https://github.com/nerel-ds/NEREL-BIO.

In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction. The NEREL collection is currently the largest Russian dataset annotated with entities and relations. It includes 933 news texts with annotation of 29 entity types and 49 relation types. The paper describes the main design principles behind NEREL’s entity linking annotation, provides its statistics, and reports evaluation results for several entity linking baselines. To date, 38,152 entity mentions in 933 documents are linked to Wikidata. The NEREL dataset is publicly available.

We present the first release of TatWordNet (http://wordnet.tatar), a wordnet resource for Tatar. TatWordNet has been constructed by the combination of the expand and the merge approaches. The synsets of TatWordNet have been compiled by: (i) the automatic conversion of concepts of TatThes, a socio-political Tatar; (ii) semi-automatic translation of synsets of RuWordNet, a wordnet resource for Russian with the followed manual verification and correction; (iii) manual translation of base RuWordNet synsets; (iv) and manual translation of the all hypernyms of the previously translated RuWordNet synsets. The currents version of TatWordNet contains 18,583 synsets, 36,540 lexical entries and 49,525 senses. The resource has been published to the Linguistic Linked Open Data cloud and interlinked with the Global WordNet Grid.

The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy.On the other hand, apart from being large lexical and semantic resources, taxonomies are graph structures.Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations.In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings.We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.

In the paper we compare the structure of the Russian language thesaurus RuWord-Net with the data of a psychosemantic experiment to identify semantically close words.The aim of the study is to find out to what extent the structure of RuWordNet corresponds to the intuitive ideas of native speakers about the semantic proximity of words.The respondents were asked to list synonyms to a given word.As a result of the experiment, we found that the respondents mainly mentioned not only synonyms but words that are in paradigmatic relations with the stimuli.The words of the mental sphere were chosen for the experiment.In 95% of cases, the words characterized in the experiment as semantically close were also close according to the thesaurus.In other cases, additions to the thesaurus were proposed.

Ontologies, taxonomies, and thesauri are used in many NLP tasks.However, most studies are focused on the creation of these lexical resources rather than the maintenance of the existing ones.Thus, we address the problem of taxonomy enrichment.We explore the possibilities of taxonomy extension in a resource-poor setting and present methods which are applicable to a large number of languages.We create novel English and Russian datasets for training and evaluating taxonomy enrichment models and describe a technique of creating such datasets for other languages.

In this paper we discuss the usefulness of applying a checking procedure to existing thesauri.The procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities.We applied the procedure to more than 30 thousand words of the Russian wordnet and found some serious errors in word sense description, including inaccurate relationships and missing senses of ambiguous words.

In this paper we consider the linking procedure of Russian wordnet (RuWordNet) to Wordnet.The specificity of the procedure in our case is based on the fact that a lot of bilingual (Russian and English) lexical data have been gathered in another Russian thesaurus RuThes, which has a different structure than WordNet.Previously, RuThes has been semiautomatically transformed into RuWordNet, having the WordNet-like structure.Now, the RuThes English data are utilized to establish matching from the RuWordNet synsets to the WordNet synsets.

In this paper we consider an approach to verification of large lexical-semantic resources as WordNet.The method of verification procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities.We calculated such word similarities on the basis of a Russian news collection and Russian wordnet (RuWordNet).We applied the procedure to more than 30 thousand words and found some serious errors in word sense description, including incorrect or absent relations or missed main senses of ambiguous words.

In the paper we presented a new Russian wordnet, RuWordNet, which was semiautomatically obtained by transformation of the existing Russian thesaurus RuThes.At the first step, the basic structure of wordnets was reproduced: synsets' hierarchy for each part of speech and the basic set of relations between synsets (hyponym-hypernym, partwhole, antonyms).At the second stage, we added causation, entailment and domain relations between synsets.Also derivation relations were established for single words and the component structure for phrases included in RuWordNet.The described procedure of transformation highlights the specific features of each type of thesaurus representations.

The paper describes the structure and current state of RuThes -thesaurus of Russian language, constructed as a linguistic ontology.We compare RuThes structure with the WordNet structure, describe principles for inclusion of multiword expressions, types of relations, experiments and applications based on RuThes.For a long time RuThes has been developed within various NLP and informationretrieval projects, and now it became available for public use.