Abstract
We present a study that compares data-driven dependency parsers obtained by means of annotation projection between language pairs of varying structural similarity. We show how the partial dependency trees projected from English to Dutch, Italian and German can be exploited to train parsers for the target languages. We evaluate the parsers against manual gold standard annotations and find that the projected parsers substantially outperform our heuristic baselines by 9―25% UAS, which corresponds to a 21―43% reduction in error rate. A comparative error analysis focuses on how the projected target language parsers handle subjects, which is especially interesting for Italian as an instance of a pro-drop language. For Dutch, we further present experiments with German as an alternative source language. In both source languages, we contrast standard baseline parsers with parsers that are enhanced with the predictions from large-scale LFG grammars through a technique of parser stacking, and show that improvements of the source language parser can directly lead to similar improvements of the projected target language parser.- Anthology ID:
- L10-1500
- Volume:
- Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
- Month:
- May
- Year:
- 2010
- Address:
- Valletta, Malta
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/722_Paper.pdf
- DOI:
- Cite (ACL):
- Kathrin Spreyer, Lilja Øvrelid, and Jonas Kuhn. 2010. Training Parsers on Partial Trees: A Cross-language Comparison. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
- Cite (Informal):
- Training Parsers on Partial Trees: A Cross-language Comparison (Spreyer et al., LREC 2010)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/722_Paper.pdf