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Rule-based Coreference Resolution in German Historic Novels

Summary

Automatically tracking characters in fiction requires linking every mention of a person, including names, descriptions and pronouns, into chains. Off-the-shelf coreference tools, built mostly on news data, perform poorly on 19th-century German novels. These texts have longer sentences, more pronouns, extensive dialogue, and a few main characters who recur constantly. Krug and colleagues adapt the multi-pass sieve design of Lee et al. (2011) to this genre, expanding it to 11 rule passes restricted to person entities. The passes cover string and name-variant matching, head and title matching, GermaNet synonyms, constraint-based pronoun resolution, and the identification of speakers and addressees in direct speech. The authors annotated their own corpus of 130-sentence novel excerpts. On it, the system clearly outperforms CorZu and stays stable on a separate unseen test set. Because the system is rule-based, its decisions can be traced, which supports systematic error analysis. The authors also intend to use it to bootstrap a larger annotated corpus for later machine-learning work.

Key points

  • Corpus: The data consists of excerpts of 130 consecutive sentences, each taken from a different novel. Five excerpts were used for development. The first test set has 48 excerpts (about 143,000 tokens and 19,000 references). A second, unseen test set has 30 excerpts (about 104,000 tokens and 11,600 entities). Roughly 70% of mentions are pronouns.
  • Pipeline: Preprocessing uses TreeTagger, OpenNLP and the MATE parser. Gender and number are assigned through a precision-ordered cascade of name lists, a maximum entropy model trained on TIGER, MATE morphology and SMOR.
  • Comparison with CorZu (48 excerpts): The system reaches a MUC F1 of 85.5% against CorZu's 65.9%, and a B³ F1 of 56.0% against 33.6%.
  • Robustness and pass analysis: On the 30 unseen excerpts, scores are MUC F1 86% and B³ F1 55.5%. Adding the pronoun pass alone raises MUC F1 from 38.9% to 83.3%.
  • Error analysis (5 excerpts): Semantic or world-knowledge errors account for 37% of mistakes. Heuristic errors, such as salience and parser problems, make up 35%. Gender, number and person errors contribute 14%, and direct-speech errors another 14%.

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

8 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 3 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3115/v1/w15-0711

@inproceedings{krug2015rule,
  title = {{Rule-based Coreference Resolution in German Historic Novels}},
  author = {Krug, Markus and Puppe, Frank and Jannidis, Fotis and Macharowsky, Luisa and Reger, Isabella and Weimar, Lukas},
  booktitle = {Proceedings of the Fourth Workshop on Computational Linguistics for Literature},
  year = {2015},
  doi = {10.3115/v1/w15-0711},
  url = {https://doi.org/10.3115/v1/w15-0711}
}

References

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