Egil Rønningstad

• Rønningstad, Egil; Velldal, Erik & Øvrelid, Lilja (2024). A GPT among Annotators: LLM-based Entity-Level Sentiment Annotation. I Henning, Sophie & Stede, Manfred (Red.), Proceedings of The 18th Linguistic Annotation Workshop (LAW-XVIII). Association for Computational Linguistics. ISSN 979-8-89176-073-8. s. 133–139. Fulltekst i vitenarkiv
• Rønningstad, Egil (2023). UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource languages. Association for Computational Linguistics (ACL). Annual Meeting Conference Proceedings. ISSN 0736-587X. Fulltekst i vitenarkiv
• Samuel, David; Kutuzov, Andrei; Touileb, Samia; Velldal, Erik; Øvrelid, Lilja & Rønningstad, Egil [Vis alle 8 forfattere av denne artikkelen] (2023). NorBench – A Benchmark for Norwegian Language Models. I Alumäe, Tanel & Fishel, Mark (Red.), Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa). University of Tartu. ISSN 978-99-1621-999-7. s. 618–633. Fulltekst i vitenarkiv
• Charpentier, Lucas Georges Gabriel; Wold, Sondre; Samuel, David & Rønningstad, Egil (2023). BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer. I Alumäe, Tanel & Fishel, Mark (Red.), Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa). University of Tartu. ISSN 978-99-1621-999-7.
• Mæhlum, Petter; Haug, Dag Trygve Truslew; Jørgensen, Tollef Emil; Kåsen, Andre; Nøklestad, Anders & Rønningstad, Egil [Vis alle 9 forfattere av denne artikkelen] (2022). NARC – Norwegian Anaphora Resolution Corpus. International Conference on Computational Linguistics (ICCL) (COLING). ISSN 1525-2477. 29(7), s. 48–60. Fulltekst i vitenarkiv Vis sammendrag

  Published in: Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC): https://aclanthology.org/venues/coling/. We present the Norwegian Anaphora Resolution Corpus (NARC), the first publicly available corpus annotated with anaphoric relations between noun phrases for Norwegian. The paper describes the annotated data for 326 documents in Norwegian Bokmål, together with inter-annotator agreement and discussions of relevant statistics. We also present preliminary modelling results which are comparable to existing corpora for other languages, and discuss relevant problems in relation to both modelling and the annotations themselves.

• Rønningstad, Egil; Øvrelid, Lilja & Velldal, Erik (2022). Entity-Level Sentiment Analysis (ELSA): An Exploratory Task Survey. International Conference on Computational Linguistics (ICCL) (COLING). ISSN 1525-2477. s. 6773–6783. Fulltekst i vitenarkiv Vis sammendrag

  This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document - what we refer to as Entity-Level Sentiment Analysis (ELSA). While identifying sentiment conveyed towards an entity is well researched for shorter texts like tweets, we find little to no research on this specific task for longer texts with multiple mentions and opinions towards the same entity. This lack of research would be understandable if ELSA can be derived from existing tasks and models. To assess this, we annotate a set of professional reviews for their overall sentiment towards each volitional entity in the text. We sample from data already annotated for document-level, sentence-level, and target-level sentiment in a multi-domain review corpus, and our results indicate that there is no single proxy task that provides this overall sentiment we seek for the entities at a satisfactory level of performance. We present a suite of experiments aiming to assess the contribution towards ELSA provided by document-, sentence-, and target-level sentiment analysis, and provide a discussion of their shortcomings. We show that sentiment in our dataset is expressed not only with an entity mention as target, but also towards targets with a sentiment-relevant relation to a volitional entity. In our data, these relations extend beyond anaphoric coreference resolution, and our findings call for further research of the topic. Finally, we also present a survey of previous relevant work.

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• Rønningstad, Egil & Øvrelid, Lilja (2020). Targeted Sentiment Analysis for Norwegian text. Universitetet i Oslo.

Se alle arbeider i Cristin