93 citations · 216 across the 33 of their papers we have counts for
6 papers · 1 filter
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…
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning
Blaž Škrlj, Marko Jukič, Nika Eržen +2
The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually. Current machine learning methods…
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…
JSI at the FinSim-2 task: Ontology-Augmented Financial Concept Classification
Timen Stepišnik Perdih, Senja Pollak, Blaž \v{Skrlj}
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 ma…
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…