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Coreference Resolution across Corpora: Languages, Coding Schemes, and Preprocessing Information

What it is about

The paper shows that a coreference score says as much about the corpus as about the system. The same entity-based system, CISTELL, is trained and tested under three corpus choices: language (English against Spanish, using OntoNotes and AnCora, both news), coding scheme (ACE against OntoNotes on the same 100,000-word TDT portion), and preprocessing (gold against automatic parses). The system uses four basket-growing strategies (head match, strong match, best match, weak match) and a memory-based classifier. The scores are MUC, B3 and CEAF. The rankings of the strategies disagree across the metrics: MUC rewards weak match most, B3 rewards head match most, and CEAF is split between super-strong and best match. In both datasets about 85% of entities are singletons and 15% have two or more mentions, with 3.9 mentions per multi-mention entity in AnCora and 3.5 in OntoNotes. The authors conclude that system comparison is only meaningful when corpus parameters match exactly.

Ideas

For Mova

Code: coref/src/eval.rs (three metrics side by side), coref/src/sieve.rs (variants as separate configurations), coref/src/corefud.rs (scheme conversion). Chapter: 35. Reference and coreference. Measure: the sieve under two scheme settings and three metrics, with the rankings printed; run twice and compare the output files byte for byte.

Topics: Coreference resolution

Cite

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

@inproceedings{recasens2010coreference,
  title = {{Coreference Resolution across Corpora: Languages, Coding Schemes, and Preprocessing Information}},
  author = {Recasens, Marta and Hovy, Eduard},
  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/P10-1144/}
}

References

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