Search Results for author: Boshko Koloski

Found 9 papers, 3 papers with code

Interesting cross-border news discovery using cross-lingual article linking and document similarity

no code implementations EACL (Hackashop) 2021 Boshko Koloski, Elaine Zosa, Timen Stepišnik-Perdih, Blaž Škrlj, Tarmo Paju, Senja Pollak

Team Name: team-8 Embeddia Tool: Cross-Lingual Document Retrieval Zosa et al. Dataset: Estonian and Latvian news datasets abstract: Contemporary news media face increasing amounts of available data that can be of use when prioritizing, selecting and discovering new news.

E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression

no code implementations LTEDI (ACL) 2022 Ilija Tavchioski, Boshko Koloski, Blaž Škrlj, Senja Pollak

Depression is a mental illness that negatively affects a person’s well-being and can, if left untreated, lead to serious consequences such as suicide.

AutoML

Retrieval-efficiency trade-off of Unsupervised Keyword Extraction

1 code implementation15 Aug 2022 Blaž Škrlj, Boshko Koloski, Senja Pollak

Efficiently identifying keyphrases that represent a given document is a challenging task.

Keyword Extraction

Out of Thin Air: Is Zero-Shot Cross-Lingual Keyword Detection Better Than Unsupervised?

no code implementations14 Feb 2022 Boshko Koloski, Senja Pollak, Blaž Škrlj, Matej Martinc

We find that the pretrained models fine-tuned on a multilingual corpus covering languages that do not appear in the test set (i. e. in a zero-shot setting), consistently outscore unsupervised models in all six languages.

Keyword Extraction Pretrained Multilingual Language Models

Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

2 code implementations20 Oct 2021 Boshko Koloski, Timen Stepišnik-Perdih, Marko Robnik-Šikonja, Senja Pollak, Blaž Škrlj

Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness.

Classification Fake News Detection +4

Extending Neural Keyword Extraction with TF-IDF tagset matching

1 code implementation EACL (Hackashop) 2021 Boshko Koloski, Senja Pollak, Blaž Škrlj, Matej Martinc

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.

Keyword Extraction

Identification of COVID-19 related Fake News via Neural Stacking

no code implementations11 Jan 2021 Boshko Koloski, Timen Stepišnik Perdih, Senja Pollak, Blaž Škrlj

Identification of Fake News plays a prominent role in the ongoing pandemic, impacting multiple aspects of day-to-day life.

Fake News Detection General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.