CAJ | 학술논문

专家证据文档识别是专家检索的关键步骤。融合专家候选文档独立页面特征以及页面之间的关联关系，提出了一个专家证据文档识别无向图模型。该方法首先分析各类专家证据文档中的词、URL 链接、专家元数据等独立页面特征以及候选专家证据文档间的链接和内容等关联关系；然后将独立页面特征以及页面之间的关联关系融入到无向图中构建专家证据文档识别无向图模型；最后利用梯度下降方法学习模型中特征的权重，并利用吉布斯采样方法进行专家证据文档识别。通过对比实验验证了该方法的有效性。实验结果表明，该方法有较好的效果。
전가증거문당식별시전가검색적관건보취。융합전가후선문당독립혈면특정이급혈면지간적관련관계，제출료일개전가증거문당식별무향도모형。해방법수선분석각류전가증거문당중적사、URL 련접、전가원수거등독립혈면특정이급후선전가증거문당간적련접화내용등관련관계；연후장독립혈면특정이급혈면지간적관련관계융입도무향도중구건전가증거문당식별무향도모형；최후이용제도하강방법학습모형중특정적권중，병이용길포사채양방법진행전가증거문당식별。통과대비실험험증료해방법적유효성。실험결과표명，해방법유교호적효과。
Expert evidence document recognition is the key step for expert search. Combining specialist candidate document independent page features and correlation among pages, this paper proposes an expert evidence document recognition method based on undirected graph model. First, independent page features such as words, URL links and expert metadata in all kinds of expert evidence document, and correlations such as links and content among candidate expert evidence document are analyzed. Then, independent page features and correlation among pages are integrated into the undirected graph to construct an undirected graph model for expert evidence document recognition. Finally, feature weights are learned in the model by using the gradient descent method and expert evidence document recognition is achieved by utilizing Gibbs Sampling method. The effectiveness of the proposed method is verified by comparison experiment. The experimental results show that the proposed method has a better effect.