Petter Mæhlum

• Touileb, Samia; Murstad, Jeanette; Mæhlum, Petter; Steskal, Lubos; Lilja Charlotte, Storset & You, Huiling [Vis alle 7 forfattere av denne artikkelen] (2024). EDEN: A Dataset for Event Detection in Norwegian News. I Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani & Xue, Nianwen (Red.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL. ISSN 978-2-493814-10-4.
• Mæhlum, Petter; Samuel, David; Norman, Rebecka Maria; Jelin, Elma; Bjertnæs, Øyvind Andresen & Øvrelid, Lilja [Vis alle 7 forfattere av denne artikkelen] (2024). It’s Difficult to be Neutral – Human and LLM-based Sentiment Annotation of Patient Comments, Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health). ELRA Language Resources Association. ISSN 978-2-493814-25-8.
• Wold, Sondre; Mæhlum, Petter & Hove, Oddbjørn (2024). Estimating Lexical Complexity from Document-Level Distributions. I Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani & Xue, Nianwen (Red.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). European Language Resources Association. ISSN 9782493814104. s. 6309–6318.
• Mæhlum, Petter & Ivanova, Sardana (2023). Phonotactics as an Aid in Low Resource Loan Word Detection and Morphological Analysis in Sakha. I Ilinykh, Nikolai; Morger, Felix; Dannélls, Dana; Dobnik, Simon; Megyesi, Beáta & Nivre, Joakim (Red.), Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023). Association for Computational Linguistics. ISSN 978-1-959429-73-9. s. 111–120. Vis sammendrag

  Obtaining information about loan words and irregular morphological patterns can be difficult for low-resource languages. Using Sakha as an example, we show that it is possible to exploit known phonemic regularities such as vowel harmony and consonant distributions to identify loan words and irregular patterns, which can be helpful in rule-based downstream tasks such as parsing and POS-tagging. We evaluate phonemically inspired methods for loanword detection, combined with bi-gram vowel transition probabilities to inspect irregularities in the morphology of loanwords. We show that both these techniques can be useful for the detection of such patterns. Finally, we inspect the plural suffix -ЛАр [-LAr] to observe some of the variation in morphology between native and foreign words.

• Barnes, Jeremy Claude; Touileb, Samia; Mæhlum, Petter & Lison, Pierre (2023). Identifying Token-Level Dialectal Features in Social Media. 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. Fulltekst i vitenarkiv Vis sammendrag

  Dialectal variation is present in many human languages and is attracting a growing interest in NLP. Most previous work concentrated on either (1) classifying dialectal varieties at the document or sentence level or (2) performing standard NLP tasks on dialectal data. In this paper, we propose the novel task of token-level dialectal feature prediction. We present a set of fine-grained annotation guidelines for Norwegian dialects, expand a corpus of dialectal tweets, and manually annotate them using the introduced guidelines. Furthermore, to evaluate the learnability of our task, we conduct labeling experiments using a collection of baselines, weakly supervised and supervised sequence labeling models. The obtained results show that, despite the difficulty of the task and the scarcity of training data, many dialectal features can be predicted with reasonably high accuracy.

• Mæhlum, Petter; Velldal, Erik & Øvrelid, Lilja (2023). A Diagnostic Dataset for Sentiment and Negation Modeling for Norwegian. I Ilinykh, Nikolai; Morger, Felix; Dannélls, Dana; Dobnik, Simon; Megyesi, Beáta & Nivre, Joakim (Red.), Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023). Association for Computational Linguistics. ISSN 978-1-959429-73-9. Fulltekst i vitenarkiv
• 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.

• Mæhlum, Petter; Kåsen, Andre; Touileb, Samia & Barnes, Jeremy (2022). Annotating Norwegian language varieties on Twitter for Part-of-speech. I Scherrer, Yves; Jauhiainen, Tommi; Ljubeši ́c, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (Red.), Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2022). Association for Computational Linguistics. ISSN 1525-2477. s. 64–69.
• Kutuzov, Andrei; Touileb, Samia; Mæhlum, Petter; Enstad, Tita & Witteman, Alexandra (2022). NorDiaChange: Diachronic Semantic Change Dataset for Norwegian, Proceedings of the Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. s. 2563–2572. Fulltekst i vitenarkiv Vis sammendrag

  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).

• Barnes, Jeremy; Mæhlum, Petter & Touileb, Samia (2021). NorDial: A Preliminary Corpus of Written Norwegian Dialect Use. I Dobnik, Simon & Øvrelid, Lilja (Red.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. s. 445–451. Vis sammendrag

  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.

• Mæhlum, Petter; Barnes, Jeremy; Kurtz, Robin; Øvrelid, Lilja & Velldal, Erik (2021). Negation in Norwegian: an annotated dataset . I Dobnik, Simon & Øvrelid, Lilja (Red.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. s. 299–308.
• Øvrelid, Lilja; Mæhlum, Petter; Barnes, Jeremy & Velldal, Erik (2020). A Fine-Grained Sentiment Dataset for Norwegian. I Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan & Piperidis, Stelios (Red.), Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-34-4.
• Mæhlum, Petter; Barnes, Jeremy Claude; Øvrelid, Lilja & Velldal, Erik (2019). Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian. I Hartmann, Mareike & Plank, Barbara (Red.), Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-995-8. s. 121–130. Fulltekst i vitenarkiv Vis sammendrag

  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.

Se alle arbeider i Cristin

• Mæhlum, Petter & Eivind, Torgersen (2022). Det er mange måter å si nei på – her er noen av de fineste . [Internett]. titan.no.
• Eivind, Torgersen & Mæhlum, Petter (2022). Ordet «ikke» skaper trøbbel for kunstig intelligens. [Internett]. forskning.no.

Se alle arbeider i Cristin