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Tree Kernels for Semantic Role Labeling

Summary

Semantic role labeling (SRL) systems usually depend on many hand-crafted syntactic features, each needing its own extraction code. This article asks whether tree kernels can automate that feature engineering. The authors split a kernel into two parts. Canonical mappings turn a parse tree into a structure that reflects predicate–argument information. Feature extraction functions then expand that structure into all of its sub-fragments. They design several mappings for different stages of an SRL pipeline: boundary detection, role classification, and re-ranking of whole annotations. Each was tested with support vector machines on the CoNLL 2005 shared task data (PropBank), both alone and combined with standard attribute–value features. Tree kernels consistently helped. The gains were largest with little training data, where the kernels act as a back-off model for sparse features. In re-ranking, they let the authors build structural features that would be hard to specify by hand. The broader point is that tree kernels offer a generic, portable route to feature design for many NLP tasks, not just SRL.

Key points

  • With limited training data, the improved kernel based on the AST m1 mapping added about 5 absolute points over a state-of-the-art boundary classifier. For argument classification, the gain was smaller but consistent, around 3%.
  • Marking relevant parse-tree nodes addresses a known weakness of tree kernels in boundary detection, where correct and incorrect candidates otherwise share most of their tree structure.
  • Structured features in the re-ranker gave about 2.5 points over the baseline. The model's theoretical upper bound was still roughly 6 points higher.
  • The re-ranker was trained on just 2 of the 22 training sections, yet matched CoNLL 2005 systems that do not use tree kernels. Adding local standard features to it reduced accuracy by about 0.5 points on Sections 12 and 24.
  • Proposed future work includes:
    • pairing the ST, SST, and PT extraction functions with the new mappings, including PT on dependency trees;
    • training on the full CoNLL 2005 data;
    • moving the most useful fragments into explicit feature spaces for speed;
    • using multiple parse views of each sentence.

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: Natural language processing

Topics: Semantic role labeling

Institutions

🔬Fondazione Bruno KesslerTrento · Italy18 works · 42 903 citations🎓University of TrentoTrento · Italy50 works · 13 649 citations🎓University of Rome Tor VergataRome · Italy5 works · 1 207 citations

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli.2008.34.2.193

@article{moschitti2008tree,
  title = {{Tree Kernels for Semantic Role Labeling}},
  author = {Moschitti, Alessandro and Pighin, Daniele and Basili, Roberto},
  journal = {Computational Linguistics},
  year = {2008},
  doi = {10.1162/coli.2008.34.2.193},
  url = {https://doi.org/10.1162/coli.2008.34.2.193}
}

References

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