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Found 46 papers, 13 papers with code
Comparison of Genres in Word Sense Disambiguation using Automatically Generated Text Collections
The best approaches in Word Sense Disambiguation (WSD) are supervised and rely on large amounts of hand-labelled data, which is not always available and costly to create.
Comparing Two Thesaurus Representations for Russian
In the paper we presented a new Russian wordnet, RuWordNet, which was semi-automatically obtained by transformation of the existing Russian thesaurus RuThes.
Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions
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.
Entity Linking over Nested Named Entities for Russian
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
Linking Russian Wordnet RuWordNet to WordNet
In this paper we consider the linking procedure of Russian wordnet (RuWordNet) to Wordnet.
Thesaurus Verification Based on Distributional Similarities
In this paper we consider an approach to verification of large lexical-semantic resources as WordNet.
Comparing Similarity of Words Based on Psychosemantic Experiment and RuWordNet
In the paper we compare the structure of the Russian language thesaurus RuWordNet with the data of a psychosemantic experiment to identify semantically close words.
Large Language Models in Targeted Sentiment Analysis
Reasoning capabilities of the fine-tuned Flan-T5 models with THoR achieve at least 5% increment with the base-size model compared to the results of the zero-shot experiment.
Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models
This article investigates a zero-shot approach to hypernymy prediction using large language models (LLMs).
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts
The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
RuArg-2022: Argument Mining Evaluation
Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts.
RuNNE-2022 Shared Task: Recognizing Nested Named Entities
In the test set the frequency of all entity types is even.
Taxonomy Enrichment with Text and Graph Vector Representations
With the rapid growth of lexical resources for specific domains, the problem of automatic extension of the existing knowledge bases with new words is becoming more and more widespread.
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
Transfer Learning for Improving Results on Russian Sentiment Datasets
In this study, we test transfer learning approach on Russian sentiment benchmark datasets using additional train sample created with distant supervision technique.
Studying Taxonomy Enrichment on Diachronic WordNet Versions
Ontologies, taxonomies, and thesauri are used in many NLP tasks.
Improving Results on Russian Sentiment Datasets
In this study, we test standard neural network architectures (CNN, LSTM, BiLSTM) and recently appeared BERT architectures on previous Russian sentiment evaluation datasets.
Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task.
Studying Attention Models in Sentiment Attitude Extraction Task
In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task.
Sentiment Frames for Attitude Extraction in Russian
Texts can convey several types of inter-related information concerning opinions and attitudes.
RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language
This paper describes the results of the first shared task on taxonomy enrichment for the Russian language.
Named Entity Recognition in Information Security Domain for Russian
The named entity recognition systems have been evaluated and compared to determine the most efficient one.
Distant Supervision for Sentiment Attitude Extraction
News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation.
Corpus-based Check-up for Thesaurus
In this paper we discuss the usefulness of applying a checking procedure to existing thesauri.
Extracting Sentiment Attitudes From Analytical Texts
In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations.
RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language.
RUSSE: The First Workshop on Russian Semantic Similarity
The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference.
Human Associations Help to Detect Conventionalized Multiword Expressions
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 phrase and its component words.
Human and Machine Judgements for Russian Semantic Relatedness
On the one hand, humans easily make judgments about semantic relatedness.
Combining Thesaurus Knowledge and Probabilistic Topic Models
In this paper we present the approach of introducing thesaurus knowledge into probabilistic topic models.
Creating a General Russian Sentiment Lexicon
The paper describes the new Russian sentiment lexicon - RuSentiLex.
Summarizing News Clusters on the Basis of Thematic Chains
In this paper we consider a method for extraction of sets of semantically similar language expressions representing different partici-pants of the text story ― thematic chains.
Automatic Term Recognition Needs Multiple Evidence
In this paper we argue that the automatic term extraction procedure is an inherently multifactor process and the term extraction models needs to be based on multiple features including a specific type of a terminological resource under development.
