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Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus

Summary

This paper describes the author's two submissions to track 2 of the CRAC 2018 shared task on bridging anaphora resolution. The task is to link anaphoric noun phrases to related but non-identical antecedents, and the data is the second release of the ARRAU corpus. The author first re-implemented the rule-based system of Hou et al. (2014), which was the best-performing approach on ISNotes. On ARRAU it performed badly.

Analysis traced the drop to annotation differences. Hou's rules mostly find referential bridging, where the anaphor depends on context to be understood. ARRAU, by contrast, largely annotates lexical bridging, such as subset, element and meronymy relations between concepts.

The rules were then revised to cover lexical bridging and compared with a learning-based model that uses equivalent information. The adapted rule-based system gives reasonable results on anaphor detection, anaphora resolution and full bridging resolution in all three ARRAU domains. It beats the learner in every setting, reaching 39.8% accuracy on the main RST anaphora resolution task.

The work shows that "bridging" is not a single, consistently annotated phenomenon. This matters for anyone reusing systems or datasets across corpora.

Key points

  • ARRAU (second release): about 350K tokens and 5512 bridging anaphors across three domains:
    • RST (news): 3777
    • TRAINS (dialogue): 710
    • PEAR stories (narratives): 333
  • Data preparation: the corpus was converted from MMAX into a CoNLL-12-style format. Problematic cases were removed, including multiple or empty antecedents and discontinuous markables. This left 3303 anaphors in RST, 558 in TRAINS and 303 in PEAR.
  • Re-implementation check: the Hou et al. (2014) re-implementation scores F1 17.8 for full bridging resolution on ISNotes, close to the 18.4 originally reported.
  • Learning-based model: Weka's SMO classifier with markable and pair features, including WordNet relations, semantic connectivity and word2vec cosine similarity.
  • Comparison: the rule-based system outperformed the learner in all settings, with significance tested by a Wilcoxon signed rank test at the 0.01 level. It often scored higher on the small PEAR and TRAINS sets than on RST.

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

10 papers of our library cite it (of the 140673 whose bibliographies we read); 7 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 10.18653/v1/w18-0703

@inproceedings{roesiger2018rule,
  title = {{Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus}},
  author = {Roesiger, Ina},
  booktitle = {Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference},
  year = {2018},
  doi = {10.18653/v1/w18-0703},
  url = {https://doi.org/10.18653/v1/w18-0703}
}

References

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