基于神经网络的微生物生长环境关系抽取方法

Bacteria Biotope Extraction on the Basis of Neural Network

提出一种基于神经网络的方法实现细菌和栖息地的关系抽取,充分利用神经网络的特性实现对隐含的深层特征的自动学习,以避免传统人工特征设计的复杂性和冗余性.该方法利用单词以及实体属性的分布式向量丰富句法和语义信息,使用两个不同神经网络模型从不同角度进行关系抽取,并融合文档级别的分类结果,在生物医学自然语言处理Bio NLP-ST 2016共享任务的BB-event语料上进行实验,取得了不错的F1值,表明该方法在微生物生长环境关系抽取上具有良好的性能.

Proposed in this paper is a neural network-based method for extracting the relationship between bacteria and their habitats. In this method,implicit senior features are learnt automatically to avoid the complexity and redundancy of the traditional artificial design of features,and,distributed vector representation with rich syntactic and semantic knowledge for words and entities,two different neural network models,as well as integrated document-level extraction results,are comprehensively employed to make an evaluation on the BB-event corpus from Bio NLP-ST 2016. Experimental results show that the proposed method achieves preferable F1 score,which means that it is effective in bacteria biotope extraction.