Planarized sentence representation for nested named entity recognition |
| |
| Institution: | 1. Business School, Yangzhou University, Yangzhou, 225000, PR. China;2. School of Economics and Management, Nanjing Technology University, Nanjing, 210000, PR. China;3. Department of Computer Science and Engineering, School of Sciences, European University Cyprus, Nicosia 1516, Cyprus;4. Positive Computing Research Group, Institute of Autonomous Systems, Department of Computer & Information Sciences, Universiti Teknologi Petronas, 32610, Bandar Seri Iskandar, Perak, Malaysia;5. Institute of IR4.0 (IIR4.0), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia |
| |
| Abstract: | One strategy to recognize nested entities is to enumerate overlapped entity spans for classification. However, current models independently verify every entity span, which ignores the semantic dependency between spans. In this paper, we first propose a planarized sentence representation to represent nested named entities. Then, a bi-directional two-dimensional recurrent operation is implemented to learn semantic dependencies between spans. Our method is evaluated on seven public datasets for named entity recognition. It achieves competitive performance in named entity recognition. The experimental results show that our method is effective to resolve nested named entities and learn semantic dependencies between them. |
| |
| Keywords: | Named entity recognition Sentence representation Self-cross encoding Planarized sentence representation |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
