Abstract
In this paper we describe the system by METU team for universal dependency parsing of multilingual text. We use a neural network-based dependency parser that has a greedy transition approach to dependency parsing. CCG supertags contain rich structural information that proves useful in certain NLP tasks. We experiment with CCG supertags as additional features in our experiments. The neural network parser is trained together with dependencies and simplified CCG tags as well as other features provided.- Anthology ID:
- K17-3023
- 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:
- 218–227
- Language:
- URL:
- https://aclanthology.org/K17-3023
- DOI:
- 10.18653/v1/K17-3023
- Cite (ACL):
- Burak Kerim Akkus, Heval Azizoglu, and Ruket Cakici. 2017. Initial Explorations of CCG Supertagging for Universal Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 218–227, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Initial Explorations of CCG Supertagging for Universal Dependency Parsing (Akkus et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/K17-3023.pdf
- Data
- Penn Treebank, Universal Dependencies