1-2hit |
Named Entity Recognition (NER) systems are often realized by supervised methods such as CRF and neural network methods, which require large annotated data. In some domains that small annotated training data is available, multi-domain or multi-task learning methods are often used. In this paper, we explore the methods that use news domain and Chinese Word Segmentation (CWS) task to improve the performance of Chinese named entity recognition in weibo domain. We first propose two baseline models combining multi-domain and multi-task information. The two baseline models share information between different domains and tasks through sharing parameters simply. Then, we propose a Double ADVersarial model (DoubADV model). The model uses two adversarial networks considering the shared and private features in different domains and tasks. Experimental results show that our DoubADV model outperforms other baseline models and achieves state-of-the-art performance compared with previous works in multi-domain and multi-task situation.
Xiubin ZHANG Yun HU Yinglu ZHANG
A method for expanding the channels of data acquisition unit used in distributed microcomputer data measure & control systems and a technique to call assembly routines by C Language are introduced in the paper. The method may increase the number of data acquisition points ten to hundreds times. So it may raise the price performance ratio of all distributed data measure & control system greatly. And the programming method may optimize program performance.