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Petter Mæhlum; Andre Kåsen; Samia Touileb; Jeremy Barnes
Annotating Norwegian language varieties on Twitter for Part-of-speech Workshop
2022.
Abstract | BibTeX | Tags: Natural-language processing (NLP), WP5: Norwegian Language Technologies | Links:
@workshop{Mæhlum2022,
title = {Annotating Norwegian language varieties on Twitter for Part-of-speech},
author = {Petter Mæhlum and Andre Kåsen and Samia Touileb and Jeremy Barnes},
url = {https://mediafutures.no/2022-vardial-1-7/},
year = {2022},
date = {2022-10-24},
urldate = {2022-10-24},
abstract = {Norwegian Twitter data poses an interesting challenge for Natural Language Processing (NLP) tasks. These texts are difficult for models trained on standardized text in one of the two Norwegian written forms (Bokmål and Nynorsk), as they contain both the typical variation of social media text, as well as a large amount of dialectal variety. In this paper we present a novel Norwegian Twitter dataset annotated with POS-tags. We show that models trained on Universal Dependency (UD) data perform worse when evaluated against this dataset, and that models trained on Bokmål generally perform better than those trained on Nynorsk. We also see that performance on dialectal tweets is comparable to the written standards for some models. Finally we perform a detailed analysis of the errors that models commonly make on this data.},
keywords = {Natural-language processing (NLP), WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {workshop}
}
Arne Berven; Ole A. Christensen; Sindre Moldeklev; Andreas L. Opdahl; Kjetil A. Villanger
A knowledge-graph platform for newsrooms Journal Article
In: Computers in Industry, vol. 123, no. 103321, 2020, (Pre SFI).
Abstract | BibTeX | Tags: Computational journalism, Journalistic knowledge platforms, Knowledge graphs, Machine learning (ML), Natural-language processing (NLP), Newsroom systems, Ontology, OWL, RDF, Semantic technologies, WP3: Media Content Production and Analysis | Links:
@article{Berven2020,
title = {A knowledge-graph platform for newsrooms},
author = {Arne Berven and Ole A. Christensen and Sindre Moldeklev and Andreas L. Opdahl and Kjetil A. Villanger },
url = {https://reader.elsevier.com/reader/sd/pii/S0166361520305558?token=F8A21A513C97BFF598C2755575B3C89174B3D404E2EDDD23EC37966A2754ACA1700011EBBCF52ADE2845ADBC12D40041},
doi = {https://doi.org/10.1016/j.compind.2020.103321},
year = {2020},
date = {2020-12-01},
urldate = {2020-12-01},
journal = {Computers in Industry},
volume = {123},
number = {103321},
abstract = {Journalism is challenged by digitalisation and social media, resulting in lower subscription numbers and reduced advertising income. Information and communication techniques (ICT) offer new opportunities. Our research group is collaborating with a software developer of news production tools for the international market to explore how social, open, and other data sources can be leveraged for journalistic purposes. We have developed an architecture and prototype called News Hunter that uses knowledge graphs, natural-language processing (NLP), and machine learning (ML) together to support journalists. Our focus is on combining existing data sources and computation and storage techniques into a flexible architecture for news journalism. The paper presents News Hunter along with plans and possibilities for future work.},
note = {Pre SFI},
keywords = {Computational journalism, Journalistic knowledge platforms, Knowledge graphs, Machine learning (ML), Natural-language processing (NLP), Newsroom systems, Ontology, OWL, RDF, Semantic technologies, WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {article}
}