---
title: "IMS HotCoref DE — Roesiger 2016"
description: "German coreference resolution has received much less attention than English."
authors: ["Ina Roesiger", "Jonas Kuhn"]
year: 2016
venue: "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)"
source: "https://aclanthology.org/L16-1024/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/roesiger-2016-ims-hotcoref-de-data-driven/"
---
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# IMS HotCoref DE: A Data-driven Co-reference Resolver for German

**Authors:** [Ina Roesiger](https://md.research.elamur.ai/free/en/authors/ina-roesiger/), [Jonas Kuhn](https://md.research.elamur.ai/free/en/authors/jonas-kuhn/) · **Year:** 2016 · **Venue:** Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L16-1024/) · [PDF](https://aclanthology.org/L16-1024.pdf) · [OpenAlex](https://openalex.org/W7160252322)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [9](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
German coreference resolution has received much less attention than English. After the SemEval 2010 shared task, only a few German systems appeared, and these were mostly rule-based or tied to a specific domain. The authors address this gap by porting the English IMS HotCoref resolver to German. That resolver treats a document's coreference structure as a rooted tree and learns with latent antecedents and non-local features. The learning algorithm stays the same. The adaptation work focuses on preprocessing and on features for German-specific difficulties: rich inflection, grammatical gender on inanimate nouns, and single-word compounds. Lexical-semantic knowledge comes from GermaNet and from word embeddings. The system is trained and tested on the TüBa-D/Z newspaper treebank. With gold annotations it reaches a CoNLL score of 65.76 on version 10. In a post-task SemEval 2010 evaluation it outperforms the previous best system, CorZu. Ablation experiments indicate that the linguistically motivated features help. The tool is released for free download, which makes it a practical, reproducible baseline for German coreference.

## Key points
- **Data and splits:** The system uses TüBa-D/Z, a gold-annotated newspaper corpus of about 1.8M tokens.
  - Version 8 is used with the official SemEval splits.
  - Version 10 is split by the authors in a 20-20-60 test-dev-train ratio (727/727/2190 documents).
- **Results on version 10:** The CoNLL score is 65.76 with gold annotations and 48.54 with predicted ones. Mention extraction recall is about 99% with gold input versus about 78% with predicted input, with most misses caused by parsing errors.
- **SemEval 2010 post-task (TüBa 8):** The system scores 63.61 in the open gold track and 48.61 in the open regular track. CorZu scores 58.11 and 45.82, and the gap is statistically significant.
- **German-specific features:**
  - All features are based on lemmas rather than word forms.
  - A gender agreement feature is added.
  - Compound head matching is implemented in two ways: a simple suffix heuristic and a version based on the COMPOST splitter.
  - GermaNet is used to look up synonyms, hypernyms and hyponyms.
  - Person and name lists from Klenner and Tuggener (2011) supply animacy and name-gender information.
- **Distributional feature:** A CBOW model is trained on lemmatised SdeWaC (tagged with TreeTagger) using lemma-POS pairs, 300 dimensions and negative sampling of 15. Two mentions count as similar when their cosine similarity, computed with DISSECT, is at least 0.8.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

9 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 6 not yet.

- [Investigating Multilingual Coreference Resolution by Universal Annotations](https://md.research.elamur.ai/free/en/papers/chai-2023-investigating-multilingual-coreference-resolution-universal/)
- [Neural End-to-end Coreference Resolution for German in Different Domains](https://md.research.elamur.ai/free/en/papers/coref--schroder-neural-end-end-coreference-resolution/)
- [GerDraCor-Coref: A Coreference Corpus for Dramatic Texts in German](https://md.research.elamur.ai/free/en/papers/pagel-2020-gerdracor-coref-coreference-corpus-dramatic/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{roesiger2016ims,
  title = {{IMS HotCoref DE: A Data-driven Co-reference Resolver for German}},
  author = {Roesiger, Ina and Kuhn, Jonas},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
  year = {2016},
  doi = {10.63317/5d86anb3gozo},
  url = {https://doi.org/10.63317/5d86anb3gozo}
}
```

- [← previous Improving Hypernymy Detection with an Integrated Path-based and Distributional Method](https://md.research.elamur.ai/free/en/papers/shwartz-2016-improving-hypernymy-detection-integrated-path/)
- [next → Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/2/)

## References

27 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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