基于Word2Vec词嵌入和聚类模型的安全生产事故文本案例分类

安全生产事故的分析对应急管理能力提升具有重要意义.通过对安全生产案例的语义分析,利用Word2Vec词嵌入技术和聚类模型,选用CBOW+负采样技术实现词向量,并结合安全生产事故案例分类的数据特点,通过基于半监督学习的聚类模型算法,根据事故性质的认定特点,提出了一种优化初始聚类中心的算法,并利用K-means聚类算法实现安全事故文本案例的分类.实验表明该方法较好实现安全生产的事故案例分类,并对安全生产事故的多个维度分析具有很好借鉴意义.

Text Case Classification of Safety Production Accidents Based on Word2Vec Word Embedding and Clustering Model

The analysis of safety production accidents is of great significance to the improvement of emergency management ability. Based on the semantic analysis of safety production cases, Word2Vec embedding technology and clustering model are used, CBOW + negative sampling technology is used to realize word vector, and the data characteristics of safety production accident cases classification are combined, through semi-supervised learning based clustering model algorithm, according to the characteristics of the accident nature, an optimized initial clustering center algorithm is proposed, and K-means clustering algorithm is used to classify the text cases of safety accidents. The experimental results show that the proposed method can realize the classification of accident cases, and can be used for reference in the multi-dimensional analysis of accident.