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Semantic Class Induction and Coreference Resolution

Summary

This paper asks whether a learning-based NP coreference resolver performs better when it uses semantic class (SC) knowledge learned from annotated data instead of the common shortcut of taking a noun's first WordNet sense. Ng trains a six-way SC classifier (PERSON, ORGANIZATION, GPE, FACILITY, LOCATION, OTHERS) on the BBN Entity Type Corpus, a version of the Penn Treebank WSJ texts in which ACE mentions carry SC labels. The features combine lexical cues, MINIPAR grammatical relations, IdentiFinder NE types, WordNet hypernym keywords, classes induced from appositive constructions in large unlabeled corpora, and neighbors from Lin's distributional thesaurus.

The predicted classes feed two knowledge sources: semantic class agreement (SCA) and mention status. Each is added to a decision-tree baseline either as a hard constraint or as a feature. On ACE Phase 2, the induced classes beat Soon et al.'s first-sense heuristic in both SC accuracy and coreference F-measure. The study also shows that how knowledge is encoded matters as much as which knowledge is used.

Key points

  • Training data: The SC classifier learns from the BBN Entity Type Corpus, which offers roughly five times more training material than ACE and yields 310,063 instances. Four learners were tested, among them Decision List, 1-NN (TiMBL) and SVM.
  • Gains over the heuristic: Induced SCs improve SC accuracy by about 2% over the first-sense heuristic. They also give a statistically significant 2% F-measure gain in coreference over a resolver using Soon et al.'s SCs.
  • Mention as a constraint: Filtering out non-mentions raises MUC F-measure by roughly 5–7%. For example, the baseline scores 57.0, Soon's SCs reach 62.6, and DL or SVM SCs reach 64.6.
  • SCA as a feature: Encoding SCA as a feature lifts common noun resolution accuracy by about 5–8% over the baseline. The strongest overall configuration applies mention status as a hard constraint while treating SCA as a feature, and this remains true even with oracle SCs.
  • Evaluation lesson: The MUC scorer fails to reveal the benefit of SCA. The author therefore argues for reporting both resolution accuracy and clustering-level scores.

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

21 papers of our library cite it (of the 140673 whose bibliographies we read); 10 of them have a page here, 11 not yet.

Cite

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

@inproceedings{ng2007semantic,
  title = {{Semantic Class Induction and Coreference Resolution}},
  author = {Ng, Vincent},
  booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},
  year = {2007},
  url = {https://aclanthology.org/P07-1068/}
}

References

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