Abstract
This paper presents our system submitted for the CoNLL 2017 Shared Task, “Multilingual Parsing from Raw Text to Universal Dependencies.” We ran the system for all languages with our own fully pipelined components without relying on re-trained baseline systems. To train the dependency parser, we used only the universal part-of-speech tags and distance between words, and applied deterministic rules to assign dependency labels. The simple and delexicalized models are suitable for cross-lingual transfer approaches and a universal language model. Experimental results show that our model performed well in some metrics and leads discussion on topics such as contribution of each component and on syntactic similarities among languages.- Anthology ID:
- K17-3028
- Volume:
- Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 265–273
- Language:
- URL:
- https://aclanthology.org/K17-3028
- DOI:
- 10.18653/v1/K17-3028
- Cite (ACL):
- Hiroshi Kanayama, Masayasu Muraoka, and Katsumasa Yoshikawa. 2017. A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 265–273, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task (Kanayama et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/K17-3028.pdf