Name
Abstract | ||
|---|---|---|
Dependency parsing is a fundamental problem in natural language processing. We introduce a novel dependency-parsing framework called head-pointing--based dependency parsing. In this framework, we cast the Korean dependency parsing problem as a statistical head-pointing and arc-labeling problem. To address this problem, a novel neural network called the multitask pointer network is devised for a neural sequential head-pointing and type-labeling architecture. Our approach does not require any handcrafted features or language-specific rules to parse dependency. Furthermore, it achieves state-of-the-art performance for Korean dependency parsing.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3282442 | acm transactions on asian and low-resource language information processing |
Keywords | Field | DocType |
Dependency parsing, deep learning, head pointing, multitask pointer networks | Pointer (computer programming),Computer science,Dependency grammar,Artificial intelligence,Natural language processing,Deep learning | Journal |
Volume | Issue | ISSN |
18 | 3 | 2375-4699 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sangkeun Jung | 1 | 197 | 15.23 |
| Cheon-Eum Park | 2 | 1 | 3.05 |
| Changki Lee | 3 | 279 | 26.18 |