Search Results for author: Irina Nikishina

Found 9 papers, 2 papers with code

TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Sematic Tasks

1 code implementation14 Mar 2024 Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, Alexander Panchenko, Irina Nikishina

It achieves 11 SotA results, 4 top-2 results out of 16 tasks for the Taxonomy Enrichment, Hypernym Discovery, Taxonomy Construction, and Lexical Entailment tasks.

Domain Adaptation Few-Shot Learning +3

RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language

no code implementations22 May 2020 Irina Nikishina, Varvara Logacheva, Alexander Panchenko, Natalia Loukachevitch

This paper describes the results of the first shared task on taxonomy enrichment for the Russian language.

Evaluation of Taxonomy Enrichment on Diachronic WordNet Versions

no code implementations EACL (GWC) 2021 Irina Nikishina, Natalia Loukachevitch, Varvara Logacheva, Alexander Panchenko

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.

Word Embeddings

Taxonomy Enrichment with Text and Graph Vector Representations

no code implementations21 Jan 2022 Irina Nikishina, Mikhail Tikhomirov, Varvara Logacheva, Yuriy Nazarov, Alexander Panchenko, Natalia Loukachevitch

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.

Knowledge Graphs Word Embeddings

RuArg-2022: Argument Mining Evaluation

no code implementations18 Jun 2022 Evgeny Kotelnikov, Natalia Loukachevitch, Irina Nikishina, Alexander Panchenko

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.

Argument Mining Natural Language Inference +1

Large Language Models Meet Knowledge Graphs to Answer Factoid Questions

no code implementations3 Oct 2023 Mikhail Salnikov, Hai Le, Prateek Rajput, Irina Nikishina, Pavel Braslavski, Valentin Malykh, Alexander Panchenko

Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks.

Knowledge Graphs Re-Ranking

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