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# Anders Søgaard · page 2

29 papers in our library · 2011–2026 · page 2 of 2

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/anders-sogaard/)

## Papers

The most cited first (OpenAlex and our library).

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26. **[Model-based Annotation of Coreference](https://md.research.elamur.ai/free/en/papers/aralikatte-2020-model-based-annotation-coreference/)** *(2020 · 8 citations)* Standard coreference annotation asks people to connect pronouns with earlier text spans. The authors argue this is unnatural, because readers reason over a mental model of the entities a text describes rather than over strings. They propose model-based annotation instead. Documents are first linked to a knowledge…
27. **[An Empirical Study of Differences between Conversion Schemes and Annotation Guidelines](https://md.research.elamur.ai/free/en/papers/sogaard-2013-empirical-study-differences-between-conversion/)** *(2013 · 3 citations)* Many dependency treebanks are converted from constituency treebanks, and different conversion schemes make different choices about heads and relation labels.
28. **[Few-Shot and Zero-Shot Learning for Historical Text Normalization](https://md.research.elamur.ai/free/en/papers/bollmann-2019-few-shot-zero-shot-learning/)** *(2019 · 2 citations)* The paper evaluates 63 multi-task configurations of an encoder-decoder normaliser on ten datasets in eight languages.
29. **[LLM Beliefs Are in Their Heads](https://md.research.elamur.ai/free/en/papers/mendozza-2026-llm-beliefs-are-their-heads/)** *(2026)* The paper asks whether decoder-only LLMs have internal states that could count as beliefs. It tests them against four standards from Herrmann and Levinstein (2025): Accuracy, Use, Coherence and Uniformity. The authors study six model families: GPT-2, Llama-3.1-8B, Gemma-2-9B, Yi-6B, Pythia-6.9B and GPT-J-6B, using…

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