NorEventGen : generative event extraction from Norwegian news (2025)

Abstract: In this work, we approach event extraction from Norwegian news text using a generation-based approach which formulates the task as text-to-structure generation. We present experiments assessing the effect of different modeling configurations and provide an analysis of the model predictions and typical system errors. Finally, we apply our system to a large corpus of raw news texts and analyze the resulting distribution of event structures in a fairly representative snap-shot of the Norwegian news landscape.

## introduction event extraction is a central information extraction task that is aimed at extracting structured representations of real-world event information provided in unstructured texts, commonly expressed in terms of an event trigger and its arguments in the text. while modeling approaches to this task have traditionally been based on sequence-labeling at the token level @xcite @xcite , more recent approaches have allowed for a structure decoding that is less constrained by the exact input string. in particular, the widespread adoption of pre-trained language models based on encoder-decoder architectures have allowed for the formulation of this task as text-to-structure generation @xcite . current event extraction systems typically focus on english, with noteworthy exceptions for other large languages like chinese and arabic. this focus is largely due to the availability of manually annotated datasets in these languages @xcite . the newly released norwegian event detection dataset eden @xcite ) contains manual annotation of news texts from newspapers as well as transcribed news broadcasts and enable large-scale event extraction from norwegian news sources. in this paper, we present the noreventgen system for norwegian event extraction, which builds on recent developments in the formulation of event extraction as text-to-event structure generation, mapping sentences into linearized event structures. while developing this system using the recently released eden dataset, we also evaluate a number of modeling choices related in particular to the format of the input data and the task formulation. specifically, we analyze the choice of pre-trained norwegian language model, the localization of event labels using translation and the reliance on explicit trigger word identification for event argument extraction. we provide a detailed analysis of the generated event structures and examine typical errors of our system. finally, we apply our system to a large collection of news texts from a range of different sources and provide a preliminary analysis of the extracted event structures. the paper is structured as follows. the next section presents related work, before section 3 presents a system description for our approach. we further describe experimental set-ups in section 4, and discuss the results in section 5. section 6 presents a use case for our system on a large norwegian news corpus, before we summarize our finding and contributions section 7. ## noreventgen: text to event records our system is built upon text2event @xcite , with inspiration from instructuie @xcite , as described in section 2.1 above. our system differs from text2event by applying no constraints on generation and from instructuie by using the input sequence only without instructions. this means approaching event extraction as a textto-structure problem. given the input sequence @xmath0, ..., x |x| , noreventgen directly generates the event records in a linearized, structured format with a pretrained norwegian encoder-decoder model. ## experiments in the following, we present the details of our experimental setup, and the specific experiments conducted as evaluation of our model. ## results and discussion we here present the results of noreventgen on norwegian event extraction with generative modeling. we first present the overall performance for different model configurations, before discussing the role of label translation and trigger generation, as described above. we then provide a more in-depth analysis of the generated event structures with a specific focus on invalid generations and present an error analysis for the best performing system. ## use case: event extraction from norwegian news one of the main use cases for event extraction systems is the automated analysis of large collections of news texts. an interesting question is whether the distribution of event types in newer news sources is similar to that found for the eden dataset (based on the somewhat dated news sources from the norwegian dependency treebank @xcite ). we here apply our best model foot_1 on a newly collected news corpus dubbed the norwegian mediacorpus foot_2 . the media-corpus collects millions of news articles in 2010s from three major media houses in norway: amedia, schibsted, and tv 2. given its size, the corpus provides a representative sample of the norwegian news landscape. ## conclusion in quire event ontology knowledge and generate tokens from the input sequence for event triggers and arguments, thus it is not necessary to implement constraints during the generation process. in our experiments, we also find that it is highly beneficial to localize the event ontology to the target language, in our case norwegian, and using a monolingual norwegian model is more beneficial. beyond the eden dataset, we extend our system to process a large corpus of raw norwegian news texts. by applying our model to this broader dataset, we analyze the predicted event distribution, providing insights into the types of events prevalent in norwegian news. this analysis serves as a snapshot of the norwegian news landscape and illustrates the potential applications of our approach for largescale event analysis in less-resourced languages.