1 citations · 1 across the 7 of their papers we have counts for
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Recent Advances and Future Directions in Literature-Based Discovery
Andrej Kastrin, Bojan Cestnik, Nada Lavrač
The explosive growth of scientific publications has created an urgent need for automated methods that facilitate knowledge synthesis and hypothesis generation. Literature-based dis…
From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies
Blaž Škrlj, Boshko Koloski, Senja Pollak +1
Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) enhances factual grounding and reasoning capabilities. This survey paper systematical…
Extracting domain-specific terms using contextual word embeddings
Andraž Repar, Nada Lavrač, Senja Pollak
Automated terminology extraction refers to the task of extracting meaningful terms from domain-specific texts. This paper proposes a novel machine learning approach to terminology…
Make Literature-Based Discovery Great Again through Reproducible Pipelines
Bojan Cestnik, Andrej Kastrin, Boshko Koloski +1
By connecting disparate sources of scientific literature, literature\-/based discovery (LBD) methods help to uncover new knowledge and generate new research hypotheses that cannot…
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis
Luka Andrenšek, Boshko Koloski, Andraž Pelicon +3
We investigate zero-shot cross-lingual news sentiment detection, aiming to develop robust sentiment classifiers that can be deployed across multiple languages without target-langua…
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining
Boshko Koloski, Nada Lavrač, Bojan Cestnik +3
In an era marked by a rapid increase in scientific publications, researchers grapple with the challenge of keeping pace with field-specific advances. We present the `AHAM' methodol…