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A Coreference Corpus and Resolution System for Dutch

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/L08-1094.pdf

Summary

Dutch has lacked both coreference-annotated data and resolution systems. The COREA project addresses this gap by building a Dutch coreference corpus of more than 200k words and a machine-learning resolver. The corpus covers spoken transcripts (CGN), newspaper text (DCOI) and medical encyclopedia entries (MedEnc), with both Netherlandic and Flemish sources. The annotation guidelines follow the MUC-6/MUC-7 scheme and mark identity, bridging, predicative and bound relations, using the MMAX2 tool. For training and evaluation, the authors also use the compatible KNACK-2002 corpus. The resolver focuses on identity relations and relies on memory-based preprocessing trained on CGN. It is evaluated in two ways: intrinsically through cross-validation, and extrinsically by plugging coreference information into downstream applications. In a medical relation-extraction task, the coreference features bring a small improvement. In the Joost question-answering system, a separate simple rule-based resolver substantially increases the number of extracted facts and raises QA accuracy. The data, guidelines, a browser-based visualization and a web demo are distributed through the Dutch TST Centrale.

Key points

  • Corpus composition:
    • CGN: 264 documents, about 33k tokens
    • DCOI: 105 documents, about 35k tokens
    • MedEnc: 497 documents, about 136k tokens
    • KNACK-2002 (supplementary): 267 documents, about 123k tokens
  • Inter-annotator agreement (29 doubly annotated documents):
    • IDENT: MUC F-score of 0.76
    • Bridging: 33%
    • PRED: 56%
    • BOUND: no agreement
  • Medical Relation Finder: this maximum-entropy model, trained on about 2000 MedEnc entries, improves slightly with coreference features. F-score rises from 53.03 to 53.51 on the 50-entry test set and from 59.15 to 59.60 on the 500-entry set.
  • Joost QA system: adding rule-based coreference raises extracted facts from about 93K to 145K. The new facts are much less precise than the pattern-based ones (34% vs. 86%). Even so, accuracy on QA@CLEF 2005 improves from 65% to 70%.
  • Visualization: annotations are converted from MMAX format into browser-viewable XML using XSL, CSS and JavaScript. The design is adapted from the Norwegian Bredt project.

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

Topics: Coreference resolution

Cited by

19 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 12 not yet.

Cite

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

@inproceedings{hendrickx2008coreference,
  title = {{A Coreference Corpus and Resolution System for Dutch}},
  author = {Hendrickx, Iris and Bouma, Gosse and Coppens, Frederik and Daelemans, Walter and Hoste, Veronique and Kloosterman, Geert and Mineur, Anne-Marie and Vloet, Joeri Van Der and Verschelde, Jean-Luc},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  doi = {10.63317/25uhxow4ott7},
  url = {https://doi.org/10.63317/25uhxow4ott7}
}

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