Abstract
In this paper, we propose a probabilistic parsing model that defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTMCNNs, which automatically benefits from both word- and character-level representations, by using a combination of bidirectional LSTMs and CNNs. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. By exploiting Kirchhoff’s Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straightforward end-to-end model training procedure via back-propagation. We evaluate our model on 17 different datasets, across 14 different languages. Our parser achieves state-of-the-art parsing performance on nine datasets.- Anthology ID:
- I17-1007
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 59–69
- Language:
- URL:
- https://aclanthology.org/I17-1007
- DOI:
- Cite (ACL):
- Xuezhe Ma and Eduard Hovy. 2017. Neural Probabilistic Model for Non-projective MST Parsing. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 59–69, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Neural Probabilistic Model for Non-projective MST Parsing (Ma & Hovy, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/I17-1007.pdf
- Data
- Penn Treebank