Abstract
For this year’s multilingual dependency parsing shared task, we developed a pipeline system, which uses a variety of features for each of its components. Unlike the recent popular deep learning approaches that learn low dimensional dense features using non-linear classifier, our system uses structured linear classifiers to learn millions of sparse features. Specifically, we trained a linear classifier for sentence boundary prediction, linear chain conditional random fields (CRFs) for tokenization, part-of-speech tagging and morph analysis. A second order graph based parser learns the tree structure (without relations), and fa linear tree CRF then assigns relations to the dependencies in the tree. Our system achieves reasonable performance – 67.87% official averaged macro F1 score- Anthology ID:
- K17-3015
- 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:
- 143–151
- Language:
- URL:
- https://aclanthology.org/K17-3015
- DOI:
- 10.18653/v1/K17-3015
- Cite (ACL):
- Xian Qian and Yang Liu. 2017. A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 143–151, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A non-DNN Feature Engineering Approach to Dependency Parsing – FBAML at CoNLL 2017 Shared Task (Qian & Liu, CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/K17-3015.pdf