Browsing ODA Open Digital Archive by Author "Øvrelid, Lilja"

Now showing items 1-4 of 4

    • NorNE: Annotating Named Entities for Norwegian 

      Jørgensen, Fredrik; Aasmoe, Tobias; Husevåg, Anne-Stine Ruud; Øvrelid, Lilja; Velldal, Erik (LREC Proceedings;Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), Chapter; Journal article; Peer reviewed, 2020)
      This paper presents NorNE, a manually annotated corpus of named entities which extends the annotation of the existing Norwegian Dependency Treebank. Comprising both of the official standards of written Norwegian (Bokmål and ...

    • Sentiment classification of online political discussions: a comparison of a word-based and dependency-based method 

      Hammer, Hugo Lewi; Solberg, Per Erik; Øvrelid, Lilja (Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis;5th, Peer reviewed; Chapter, 2014)
      Online political discussions have received a lot of attention over the past years. In this paper we compare two sentiment lexicon approaches to classify the sentiment of sentences from political discussions. The first ...

    • Threat detection in online discussions 

      Wester, Aksel Ladegård; Øvrelid, Lilja; Velldal, Erik; Hammer, Hugo Lewi (Chapter; Peer reviewed; Chapter, 2016)
      This paper investigates the effect of various types of linguistic features (lexical, syntactic and semantic) for training classifiers to detect threats of violence in a corpus of YouTube comments. Our ...

    • THREAT: A Large Annotated Corpus for Detection of Violent Threats 

      Hammer, Hugo Lewi; Riegler, Michael Alexander; Øvrelid, Lilja; Velldal, Erik (International Workshop on Content-Based Multimedia Indexing, CBMI;2019 International Conference on Content-Based Multimedia Indexing (CBMI), Conference object, 2019-10-21)
      Understanding, detecting, moderating and in extreme cases deleting hateful comments in online discussions and social media are well-known challenges. In this paper we present a dataset consisting of a total of around 30000 ...