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Automatic Propbank Generation for Turkish

Written by Mova (claude-opus-5-5) from the paper text. Original — aclanthology.org/R19-1005.pdf

Summary

The authors automatically build a Turkish PropBank, i.e., a corpus annotated with predicate-argument structure for the semantic role labeling task. Manual annotation of such a resource is expensive and requires linguists. They therefore transfer semantic roles from English. They take English–Turkish parallel sentences from the translated Penn Treebank (the corpus of Ak et al., 2018) and add annotations from the first version of English PropBank to them. The predicate in a Turkish sentence is identified through morphological analysis: the word must have a verbal root and a verbal last inflectional group. The English roles are then passed to it, and the argument labels are aligned with the Turkish annotation scheme. For sentences without a tree correspondence to the English ones, words are aligned via WordNet or with the statistical models IBM Model 1 and 2. Turkish roots are found with an FSM morphological analyzer, and words that remain unlabeled are labeled by rules based on case suffixes. Evaluated against manual annotation, the tree-based method achieves 64% accuracy, and about 75% among transferred labels alone. This approach quickly yields a draft PropBank for low-resource languages, leaving annotators only to proofread it.

Key points

  • Data: the original English PropBank (Palmer et al., 2004, LDC) and Turkish translations of Penn Treebank sentences. English annotations could be added to 6 060 of 9 558 sentences. For comparison, the manually annotated Turkish PropBank (Ak et al., 2018) was used: 9 560 sentences, framesets for 1 330 verbs and 1 914 senses.
  • Transfer via parallel trees achieves 64% accuracy relative to manual annotation. Counting only the transferred annotations, it is approximately 75%.
  • WordNet-based alignment has low coverage: only ~14.59% of annotations are transferred correctly. However, of the 5 457 transferred roles, 4 255 are correct, i.e., about 79%.
  • IBM Model 1 and 2, together with a boosting step and morphological rules, yield about 60% correct annotations, with accuracies of 59.44% and 59.86%.
  • The rules rely on case: a noun without a suffix becomes ARG0, the accusative or dative case gives ARG1, the locative or ablative ARGM-LOC, and the instrumental ARG2. Dates and times receive ARGM-TMP, and numerical expressions ARGM-EXT.

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: Semantic role labeling

Cited by

2 papers of our library cite it (of the 140673 whose bibliographies we read).

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.26615/978-954-452-056-4_005

@inproceedings{ak2019automatic,
  title = {{Automatic Propbank Generation for Turkish}},
  author = {AK, Koray and Yıldız, Olcay Taner},
  booktitle = {Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)},
  year = {2019},
  doi = {10.26615/978-954-452-056-4\_005},
  url = {https://doi.org/10.26615/978-954-452-056-4_005}
}

References

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