41 citations · 146 across the 15 of their papers we have counts for
16 papers
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
Natalia Loukachevitch, Suresh Manandhar, Elina Baral +5
This paper 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 da…
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch +4
The RuNNE Shared Task approaches the problem of nested named entity recognition. The annotation schema is designed in such a way, that an entity may partially overlap or even be ne…
Taxonomy Enrichment with Text and Graph Vector Representations
Irina Nikishina, Mikhail Tikhomirov, Varvara Logacheva +3
Knowledge graphs such as DBpedia, Freebase or Wikidata always contain a taxonomic backbone that allows the arrangement and structuring of various concepts in accordance with the hy…
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura +6
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it co…
Studying Taxonomy Enrichment on Diachronic WordNet Versions
Irina Nikishina, Alexander Panchenko, Varvara Logacheva +1
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 exi…
Improving Results on Russian Sentiment Datasets
Anton Golubev, Natalia Loukachevitch
In this study, we test standard neural network architectures (CNN, LSTM, BiLSTM) and recently appeared BERT architectures on previous Russian sentiment evaluation datasets. We comp…
Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
Nicolay Rusnachenko, Natalia Loukachevitch
In the sentiment attitude extraction task, the aim is to identify <<attitudes>> -- sentiment relations between entities mentioned in text. In this paper, we provide a study on atte…
Studying Attention Models in Sentiment Attitude Extraction Task
Nicolay Rusnachenko, Natalia Loukachevitch
In the sentiment attitude extraction task, the aim is to identify <<attitudes>> -- sentiment relations between entities mentioned in text. In this paper, we provide a study on atte…
Sentiment Frames for Attitude Extraction in Russian
Natalia Loukachevitch, Nicolay Rusnachenko
Texts can convey several types of inter-related information concerning opinions and attitudes. Such information includes the author's attitude towards mentioned entities, attitudes…
RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language
Irina Nikishina, Varvara Logacheva, Alexander Panchenko +1
This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. The participants were asked to extend an existing taxonomy with previousl…
Extracting Sentiment Attitudes From Analytical Texts
Natalia Loukachevitch, Nicolay Rusnachenko
In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations. For each document we annotated sentiments from the author to ment…
RUSSE: The First Workshop on Russian Semantic Similarity
Alexander Panchenko, Natalia Loukachevitch, Dmitry Ustalov +3
The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference. There exist a lot of comparativ…
RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
Alexander Panchenko, Anastasiya Lopukhina, Dmitry Ustalov +4
The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language. While similar shared tasks were conducted in the past for some Roma…
Human Associations Help to Detect Conventionalized Multiword Expressions
Natalia Loukachevitch, Anastasia Gerasimova
In this paper we show that if we want to obtain human evidence about conventionalization of some phrases, we should ask native speakers about associations they have to a given phra…
Human and Machine Judgements for Russian Semantic Relatedness
Alexander Panchenko, Dmitry Ustalov, Nikolay Arefyev +4
Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgments about semantic relatedness. On the other hand, th…
Combining Thesaurus Knowledge and Probabilistic Topic Models
Natalia Loukachevitch, Michael Nokel, Kirill Ivanov
In this paper we present the approach of introducing thesaurus knowledge into probabilistic topic models. The main idea of the approach is based on the assumption that the frequenc…