Abstract
We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task’s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this #ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.- Anthology ID:
- K17-3026
- Volume:
- Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Jan Hajič, Dan Zeman
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 243–252
- Language:
- URL:
- https://aclanthology.org/K17-3026
- DOI:
- 10.18653/v1/K17-3026
- Cite (ACL):
- Éric de La Clergerie, Benoît Sagot, and Djamé Seddah. 2017. The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 243–252, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy (de La Clergerie et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/K17-3026.pdf
- Data
- Universal Dependencies