RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts

In this paper, we introduce the Dialogue Evaluation shared task on extraction of structured opinions from Russian news texts.The task of the contest is to extract opinion tuples for a given sentence; the tuples are composed of a sentiment holder, its target, an expression and sentiment from the holder to the target.In total, the task received more than 100 submissions.The participants experimented mainly with large language models in zero-shot, fewshot and fine-tuning formats.The best result on the test set was obtained with fine-tuning of a large language model.We also compared 30 prompts and 11 open source language models with 3-32 billion parameters in the 1-shot and 10-shot settings and found the best models and prompts.

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Publication Details

Journal
Computational Linguistics and Intellectual Technologies
Published
2025-01-01
DOI
https://doi.org/10.28995/2075-7182-2025-23-234-244
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00

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article

RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts

Natalia Loukachevitch, Nicolay Rusnachenko, Mikhail Tikhomirov, Natalia Tkachenko et al.
Computational Linguistics and Intellectual Technologies
Topic Modeling
article

RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts

Natalia Loukachevitch, Nicolay Rusnachenko, Mikhail Tikhomirov, Natalia Tkachenko, Anna Lapanitsyna
article en

Abstract

In this paper, we introduce the Dialogue Evaluation shared task on extraction of structured opinions from Russian news texts.The task of the contest is to extract opinion tuples for a given sentence; the tuples are composed of a sentiment holder, its target, an expression and sentiment from the holder to the target.In total, the task received more than 100 submissions.The participants experimented mainly with large language models in zero-shot, fewshot and fine-tuning formats.The best result on the test set was obtained with fine-tuning of a large language model.We also compared 30 prompts and 11 open source language models with 3-32 billion parameters in the 1-shot and 10-shot settings and found the best models and prompts.

Computational Linguistics and Intellectual Technologies
Lomonosov Moscow State University
Openalex Percentile: Top 88%
Topic Modeling
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RuOpinionNE-2024: Extraction of Opinion Tuples from Russian News Texts — Natalia Loukachevitch, Nicolay Rusnachenko, et al. · Computational Linguistics and Intellectual Technologies (2025) | TGRS Research Map | TGRS