Name
Abstract | ||
|---|---|---|
Tibetan location name recognition is an important research topic in Tibetan information processing. In this paper, we attempt to construct a neural network model based on BiLSTM-CRF framework for location name recognition. In the experiment, for the public dataset of Tibetan location name, the model obtains the context information of the current word in the BiLSTM layer, decodes the output of the BiLSTM layer in the CRF layer, and finally outputs the tag sequence. The experiment proves the validity of the framework and obtains good experimental results, and the results show that the model outperforms competitive the traditional method, and improved the F-measure without resorting to any language-specific knowledge or resources. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CyberC.2019.00077 | 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) |
Keywords | Field | DocType |
BiLSTM,CRF,Tibetan location name,Location name Recognition | Data mining,Information processing,Computer science,Real-time computing,Artificial neural network,Decodes | Conference |
ISSN | ISBN | Citations |
2475-7020 | 978-1-7281-2543-5 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Ma | 1 | 21 | 3.58 |
| Hongzhi Yu | 2 | 4 | 5.18 |
| Kun Zhao | 3 | 103 | 21.08 |
| Deshun Zhao | 4 | 0 | 0.34 |
| Jun Yang | 5 | 36 | 27.68 |
| Jing Ma | 6 | 11 | 2.04 |