Papers by Kristoffer Nielbo
S3 - Semantic Signal Separation (2025.acl-long)
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Márton Kardos, Jan Kostkan, Kenneth Enevoldsen, Arnault-Quentin Vermillet, Kristoffer Nielbo, Roberta Rocca
| Challenge: | Recent efforts to incorporate contextual representations into topic models have been shown to outperform classical topic models. |
| Approach: | They propose a theory-driven topic modeling approach that decomposes contextualized document embeddings into a Python package that implements S3 and all contextual baselines. |
| Outcome: | The proposed model is 4.5x faster than the BERTopic model and provides diverse and highly coherent topics with no preprocessing. |
Fact from Fiction: Finding Serialized Novels in Newspapers (2025.acl-srw)
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| Challenge: | Among underrepresented but widely read forms are serialized fiction and feuilleton novels embedded in newspapers rather than published as standalone volumes. |
| Approach: | They propose to annotate 1,394 articles and evaluate classification pipelines using both selected linguistic features and embeddings to identify serialized fiction and feuilleton fiction. |
| Outcome: | The proposed methods achieve F1-scores of 0.91 in an annotated dataset of 1,394 articles and support the construction of alternative literary corpora and contribute to work on modeling the fiction–nonfiction boundary at scale. |
A Matter of Perspective: Building a Multi-Perspective Annotated Dataset for the Study of Literary Quality (2024.lrec-main)
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Yuri Bizzoni, Pascale Feldkamp Moreira, Ida Marie S. Lassen, Mads Rosendahl Thomsen, Kristoffer Nielbo
| Challenge: | a dataset collecting quality judgments on 9,000 English-language novels is presented . authors include experts opinions and crowd-sourced annotations . |
| Approach: | They propose a dataset collecting quality judgments on 9,000 English-language novels by 3,150 predominantly Anglophone authors. |
| Outcome: | The proposed dataset examines the perceived quality of 9,000 English-language novels by 3,150 predominantly anglophone authors. |