114 citations · 320 across the 17 of their papers we have counts for
18 papers
Assessing Readability with LLMs: The Role of Reasoning and Few-Shot Prompting
Raphaël Thieffry, Matej Martinc
Readability assessment is essential for tailoring texts to intended audiences across educational, healthcare, and information retrieval domains. However, traditional readability fo…
Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models
Luka Debevc, Nishan Chatterjee, Antoine Doucet +2
Large Language Models are increasingly deployed as information intermediaries, yet measuring their political behavior remains fragile because questionnaire results mix model dispos…
Benchmarking Machine Learning Approaches for Polarization Mapping in Ferroelectrics Using 4D-STEM
Matej Martinc, Goran Dražič, Anton Kokalj +5
Four-dimensional scanning transmission electron microscopy (4D-STEM) provides rich, atomic-scale insights into materials structures. However, extracting specific physical propertie…
FoodSEM: Large Language Model Specialized in Food Named-Entity Linking
Ana Gjorgjevikj, Matej Martinc, Gjorgjina Cenikj +3
This paper introduces FoodSEM, a state-of-the-art fine-tuned open-source large language model (LLM) for named-entity linking (NEL) to food-related ontologies. To the best of our kn…
SEKE: Specialised Experts for Keyword Extraction
Matej Martinc, Hanh Thi Hong Tran, Senja Pollak +1
Keyword extraction involves identifying the most descriptive words in a document, allowing automatic categorisation and summarisation of large quantities of diverse textual data. R…
Multi-Task Learning for Features Extraction in Financial Annual Reports
Syrielle Montariol, Matej Martinc, Andraž Pelicon +4
For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) info…
Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance
Marko Pranjić, Kaja Dobrovoljc, Senja Pollak +1
In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provide…
The Recent Advances in Automatic Term Extraction: A survey
Hanh Thi Hong Tran, Matej Martinc, Jaya Caporusso +2
Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of…
Ensembling Transformers for Cross-domain Automatic Term Extraction
Hanh Thi Hong Tran, Matej Martinc, Andraz Pelicon +2
Automatic term extraction plays an essential role in domain language understanding and several natural language processing downstream tasks. In this paper, we propose a comparative…
A bilingual approach to specialised adjectives through word embeddings in the karstology domain
Larisa Grčić Simeunović, Matej Martinc, Špela Vintar
We present an experiment in extracting adjectives which express a specific semantic relation using word embeddings. The results of the experiment are then thoroughly analysed and c…
Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?
Boshko Koloski, Senja Pollak, Blaž Škrlj +1
Keyword extraction is the task of retrieving words that are essential to the content of a given document. Researchers proposed various approaches to tackle this problem. At the top…
Extending Neural Keyword Extraction with TF-IDF tagset matching
Boshko Koloski, Senja Pollak, Blaž Škrlj +1
Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In…
COVID-19 therapy target discovery with context-aware literature mining
Matej Martinc, Blaž Škrlj, Sergej Pirkmajer +4
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing…
TNT-KID: Transformer-based Neural Tagger for Keyword Identification
Matej Martinc, Blaž Škrlj, Senja Pollak
With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization and summarization of these data has become a necessity. In t…
Capturing Evolution in Word Usage: Just Add More Clusters?
Matej Martinc, Syrielle Montariol, Elaine Zosa +1
The way the words are used evolves through time, mirroring cultural or technological evolution of society. Semantic change detection is the task of detecting and analysing word evo…
Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift
Matej Martinc, Petra Kralj Novak, Senja Pollak
We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddin…
Supervised and Unsupervised Neural Approaches to Text Readability
Matej Martinc, Senja Pollak, Marko Robnik-Šikonja
We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language model…
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
Blaž Škrlj, Matej Martinc, Jan Kralj +2
The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may…