16 citations · 49 across the 17 of their papers we have counts for
17 papers · 1 filter
Generative Models Enhanced by Sequence Labelling and Aspect-Code Switching Improve Cross-lingual Aspect-Based Sentiment Analysis
Jakub Šmíd, Pavel Přibáň, Pavel Král
Cross-lingual aspect-based sentiment analysis (ABSA) transfers knowledge from a source language with annotated data to a target language, enabling fine-grained sentiment analysis w…
Large Language Models for Citation Function Classification
Daniel Vodička, Jakub Šmíd, Pavel Král +1
Citation function classification plays a crucial role in understanding the relationships between scientific publications and advancing bibliometric analysis. This study presents on…
Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks
Jakub Šmíd, Pavel Přibáň, Pavel Král
This paper introduces a novel Czech dataset in the restaurant domain for aspect-based sentiment analysis (ABSA), enriched with annotations of opinion terms. The dataset supports th…
Large Language Models for the Summarization of Czech Documents: From History to the Present
Václav Tran, Jakub Šmíd, Ladislav Lenc +2
Text summarization is the task of automatically condensing longer texts into shorter, coherent summaries while preserving the original meaning and key information. Although this ta…
Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Jakub Šmíd, Pavel Přibáň, Pavel Král
While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current c…
Large Language Models for Summarizing Czech Historical Documents and Beyond
Václav Tran, Jakub Šmíd, Jiří Martínek +2
Text summarization is the task of shortening a larger body of text into a concise version while retaining its essential meaning and key information. While summarization has been si…