93 citations · 216 across the 33 of their papers we have counts for
26 papers · 1 filter
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…
Environmental, Social and Governance Sentiment Analysis on Slovene News: A Novel Dataset and Models
Paula Dodig, Boshko Koloski, Katarina Sitar Šuštar +2
Environmental, Social, and Governance (ESG) considerations are increasingly integral to assessing corporate performance, reputation, and long-term sustainability. Yet, reliable ESG…
Multilingual Cognitive Impairment Detection in the Era of Foundation Models
Damar Hoogland, Boshko Koloski, Jaya Caporusso +5
We evaluate cognitive impairment (CI) classification from transcripts of speech in English, Slovene, and Korean. We compare zero-shot large language models (LLMs) used as direct cl…
The "Right" Discourse on Migration: Analysing Migration-Related Tweets in Right and Far-Right Political Movements
Nishan Chatterjee, Veronika Bajt, Ana Zwitter Vitez +1
The rise of right-wing populism in Europe has brought to the forefront the significance of analysing social media discourse to understand the dissemination of extremist ideologies…
A Computational Framework to Identify Self-Aspects in Text
Jaya Caporusso, Matthew Purver, Senja Pollak
This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. W…
FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation
Boshko Koloski, Senja Pollak, Roberto Navigli +1
Building on the success of Large Language Models (LLMs), LLM-based representations have dominated the document representation landscape, achieving great performance on the document…
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…
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…
Transformer verbatim in-context retrieval across time and scale
Kristijan Armeni, Marko Pranjić, Senja Pollak
To predict upcoming text, language models must in some cases retrieve in-context information verbatim. In this report, we investigated how the ability of language models to retriev…
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…
CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature
Julien Delaunay, Hanh Thi Hong Tran, Carlos-Emiliano González-Gallardo +6
The growing impact of climate change on coastal areas, particularly active but fragile regions, necessitates collaboration among diverse stakeholders and disciplines to formulate e…
A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media
Jaya Caporusso, Damar Hoogland, Mojca Brglez +3
Dehumanisation involves the perception and or treatment of a social group's members as less than human. This phenomenon is rarely addressed with computational linguistic techniques…
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…
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…
Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies
Boshko Koloski, Blaž Škrlj, Marko Robnik-Šikonja +1
The cross-lingual transfer is a promising technique to solve tasks in less-resourced languages. In this empirical study, we compare two fine-tuning approaches combined with zero-sh…
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…
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…
Named entity recognition architecture combining contextual and global features
Tran Thi Hong Hanh, Antoine Doucet, Nicolas Sidere +2
Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations,...) within a document into pr…
Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles
Boshko Koloski, Timen Stepišnik-Perdih, Marko Robnik-Šikonja +2
Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance a…
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…
Identification of COVID-19 related Fake News via Neural Stacking
Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak +1
Identification of Fake News plays a prominent role in the ongoing pandemic, impacting multiple aspects of day-to-day life. In this work we present a solution to the shared task tit…