Joint Identification Method for Temporal and Causal Relations of Events
Abstrak
Based on the correlation between temporal relations and causal relations of events,this paper proposes a joint identification method using neural network.The method takes the identification of temporal relations as the main task,and that of causal relations as auxiliary task.On this basis,three types of joint identification models of sharing encoding layer,decoding layer and encoding-decoding layer in auxiliary tasks are designed to enable information sharing through the network layer of the main task model and the auxiliary task model.Then feature information of joint identification models is learnt.Experimental results show that the joint identification method can use the causal information between events to significantly improve the identification performance of temporal relations.Also,the joint identification model of sharing encoding-decoding layer in auxiliary tasks is more suitable for the joint identification of temporal and causal relations of events.
Topik & Kata Kunci
Penulis (1)
ZHANG Yijie, LI Peifeng, ZHU Qiaoming
Akses Cepat
- Tahun Terbit
- 2020
- Sumber Database
- DOAJ
- DOI
- 10.19678/j.issn.1000-3428.0054800
- Akses
- Open Access ✓