Martin Žnidaršič


pdf bib
Sentiment Classification by Incorporating Background Knowledge from Financial Ontologies
Timen Stepišnik-Perdih | Andraž Pelicon | Blaž Škrlj | Martin Žnidaršič | Igor Lončarski | Senja Pollak
Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022

Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. This paper presents a practical use of an ontology for the purpose of data set generalization in an oversampling setting, with the aim of improving classification models. We demonstrate our solution on a novel financial sentiment data set using the Financial Industry Business Ontology (FIBO). The results show that generalization-based data enrichment benefits simpler models in a general setting and more complex models such as BERT in low-data setting.


pdf bib
Preliminary experimentation with combinations and extensions of forward-looking sentence detection wordlists
Jan Štihec | Senja Pollak | Martin Žnidaršič
Proceedings of the 3rd Financial Narrative Processing Workshop

pdf bib
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions
Senja Pollak | Marko Robnik-Šikonja | Matthew Purver | Michele Boggia | Ravi Shekhar | Marko Pranjić | Salla Salmela | Ivar Krustok | Tarmo Paju | Carl-Gustav Linden | Leo Leppänen | Elaine Zosa | Matej Ulčar | Linda Freienthal | Silver Traat | Luis Adrián Cabrera-Diego | Matej Martinc | Nada Lavrač | Blaž Škrlj | Martin Žnidaršič | Andraž Pelicon | Boshko Koloski | Vid Podpečan | Janez Kranjc | Shane Sheehan | Emanuela Boros | Jose G. Moreno | Antoine Doucet | Hannu Toivonen
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program. The collected resources were offered to participants of a hackathon organized as part of the EACL Hackashop on News Media Content Analysis and Automated Report Generation in February 2021. The hackathon had six participating teams who addressed different challenges, either from the list of proposed challenges or their own news-industry-related tasks. This paper goes beyond the scope of the hackathon, as it brings together in a coherent and compact form most of the resources developed, collected and released by the EMBEDDIA project. Moreover, it constitutes a handy source for news media industry and researchers in the fields of Natural Language Processing and Social Science.