Petter Mæhlum


2022

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NARCNorwegian Anaphora Resolution Corpus
Petter Mæhlum | Dag Haug | Tollef Jørgensen | Andre Kåsen | Anders Nøklestad | Egil Rønningstad | Per Erik Solberg | Erik Velldal | Lilja Øvrelid
Proceedings of the Fifth Workshop on Computational Models of Reference, Anaphora and Coreference

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.

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NorDiaChange: Diachronic Semantic Change Dataset for Norwegian
Andrey Kutuzov | Samia Touileb | Petter Mæhlum | Tita Enstad | Alexandra Wittemann
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We describe NorDiaChange: the first diachronic semantic change dataset for Norwegian. NorDiaChange comprises two novel subsets, covering about 80 Norwegian nouns manually annotated with graded semantic change over time. Both datasets follow the same annotation procedure and can be used interchangeably as train and test splits for each other. NorDiaChange covers the time periods related to pre- and post-war events, oil and gas discovery in Norway, and technological developments. The annotation was done using the DURel framework and two large historical Norwegian corpora. NorDiaChange is published in full under a permissive licence, complete with raw annotation data and inferred diachronic word usage graphs (DWUGs).

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Annotating Norwegian language varieties on Twitter for Part-of-speech
Petter Mæhlum | Andre Kåsen | Samia Touileb | Jeremy Barnes
Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects

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.

2021

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Negation in Norwegian: an annotated dataset
Petter Mæhlum | Jeremy Barnes | Robin Kurtz | Lilja Øvrelid | Erik Velldal
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

This paper introduces NorecNeg – the first annotated dataset of negation for Norwegian. Negation cues and their in-sentence scopes have been annotated across more than 11K sentences spanning more than 400 documents for a subset of the Norwegian Review Corpus (NoReC). In addition to providing in-depth discussion of the annotation guidelines, we also present a first set of benchmark results based on a graph-parsing approach.

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NorDial: A Preliminary Corpus of Written Norwegian Dialect Use
Jeremy Barnes | Petter Mæhlum | Samia Touileb
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere. There are, however, few available resources to study this variation and its change over time and in more informal areas, on social media. In this paper, we propose a first step to creating a corpus of dialectal variation of written Norwegian. We collect a small corpus of tweets and manually annotate them as Bokmål, Nynorsk, any dialect, or a mix. We further perform preliminary experiments with state-of-the-art models, as well as an analysis of the data to expand this corpus in the future. Finally, we make the annotations available for future work.

2020

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A Fine-grained Sentiment Dataset for Norwegian
Lilja Øvrelid | Petter Mæhlum | Jeremy Barnes | Erik Velldal
Proceedings of the Twelfth Language Resources and Evaluation Conference

We here introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying texts are taken from a corpus of professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, games, music, products, movies and more. We here present a detailed description of this annotation effort. We provide an overview of the developed annotation guidelines, illustrated with examples and present an analysis of inter-annotator agreement. We also report the first experimental results on the dataset, intended as a preliminary benchmark for further experiments.

2019

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Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian
Petter Mæhlum | Jeremy Barnes | Lilja Øvrelid | Erik Velldal
Proceedings of the 22nd Nordic Conference on Computational Linguistics

This paper documents the creation of a large-scale dataset of evaluative sentences – i.e. both subjective and objective sentences that are found to be sentiment-bearing – based on mixed-domain professional reviews from various news-sources. We present both the annotation scheme and first results for classification experiments. The effort represents a step toward creating a Norwegian dataset for fine-grained sentiment analysis.