基于BP神经网络的藏语实体关系抽取

Tibetan Entity Relation Extraction Based on BP Neural Network

随着藏文信息开始与现代化接轨,藏文信息数量在网络上迅速增加。面对海量的网络信息,如何从中挖掘人们所需的信息成为目前关注的热点。目前中文实体关系抽取研究已取得较多成果,而在藏语人物属性抽取研究方面还有很大的提升空间。实验选取实体位置关系、实体间距离关系、实体及周围词特征进行特征向量化。通过BP神经网络模型进行分类抽取,并且取得了较好效果。研究成果可在搜索引擎、信息安全、机器翻译等许多应用领域发挥重要作用。

At present,Tibetan information is quickly connected with modernization and information,which brings in the expansive development of Tibetan information on the network.In the face of the massive network information,how to extract the information that people want is an urgent problem to be solved.Currently,Chinese entity relation extraction studies have some good results,but there is still much space to Tibetan entity relation extraction.The experiment selects entity location features,entity distance features,entities and surrounding word features for further vectorization.The BP neural network model was used for classification extraction and good results were obtained.This research has a very important role in the search engine,information security,machine translation and many other applications.