Tarmo Paju


2021

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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.

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Interesting cross-border news discovery using cross-lingual article linking and document similarity
Boshko Koloski | Elaine Zosa | Timen Stepišnik-Perdih | Blaž Škrlj | Tarmo Paju | Senja Pollak
Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

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. In this work we propose a methodology for retrieving interesting articles in a cross-border news discovery setting. More specifically, we explore how a set of seed documents in Estonian can be projected in Latvian document space and serve as a basis for discovery of novel interesting pieces of Latvian news that would interest Estonian readers. The proposed methodology was evaluated by Estonian journalist who confirmed that in the best setting, from top 10 retrieved Latvian documents, half of them represent news that are potentially interesting to be taken by the Estonian media house and presented to Estonian readers.