Name
Abstract | ||
|---|---|---|
In this article, we introduce a novel neural architecture for the end-to-end Korean Part-of-Speech (POS) tagging problem. To address the problem, we extend the present recurrent neural network-based sequence-to-sequence models to deal with the key challenges in this task: rare word generation and POS tagging. To overcome these issues, Input-Feeding and Copying mechanism are adopted. Although our approach does not require any manual features or preprocessed pattern matching dictionaries, our best single model achieves an F-score of 97.08. This is competitive with the current state-of-the-art model (F-score 98.03), which requires extensive manual feature processing.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3178458 | ACM Trans. Asian & Low-Resource Lang. Inf. Process. |
Keywords | Field | DocType |
Part-of-speech tagging, copying mechanism, deep learning | Architecture,End-to-end principle,Computer science,Copying mechanism,Part-of-speech tagging,Recurrent neural network,Speech recognition,Artificial intelligence,Deep learning,Pattern matching | Journal |
Volume | Issue | ISSN |
17 | 3 | 2375-4699 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sangkeun Jung | 1 | 197 | 15.23 |
| Changki Lee | 2 | 279 | 26.18 |
| Hyunsun Hwang | 3 | 0 | 0.34 |