【摘要】 针对当前新闻网页主题内容抽取方法较少利用网页块之间关联特性这一问题,提出了一种基于条件随机场(CRFs)的新闻网页主题内容自动抽取方法。该方法首先将待抽取网页解析成DOM树,经过滤、剪枝以及压缩处理后,然后按照一定的启发式规则将DOM树切割成块并转换成为一个数据序列,再定义CRFs特征函数来提取各网页块自身状态特征和相邻块间类别转移特征,通过CRFs模型对数据序列进行类别标注来实现网页主题内容的抽取。实验表明,该方法对新闻网页主题内容的抽取具有较高检准率和较强适应能力,引入块间关联特性可改善新闻网页主题内容的抽取。更多还原

【Abstract】 Most previous works on Web information extraction seldom use associations among Web page blocks.In order to solve this problem,this paper proposes an automatic Web news content extraction approach based on conditional random fields(CRFs).Firstly,it parses a target news page to a DOM tree.After eliminating invalid nodes,pruning subtrees and deleting single nodes in the tree,it uses heuristic rules to segment the DOM tree to blocks and converts these blocks into a data sequence.Then,it defines feature functions to extract each block’s own state features and neighbor blocks’ category transition features.Finally,by labeling the data sequence based on CRFs,it identifies each block’s category to extract the page’s content.Experimental results indicate that this approach is precise and adaptable for Web news content extraction,and importing associations among page blocks can improve Web news content extraction.更多还原