基于文本密度模型的Web正文抽取
Web Content Extraction Based on Text Density Model

朱泽德 ¹ 李淼 ² ^* 张健 ² 陈雷 ² 曾新华 ²

文摘	为从大量无关信息中获取有用内容,正文抽取成为Web数据应用不可或缺的组成部分。文中提出一种基于文本密度模型的新闻网页正文抽取方法。主要通过融合网页结构和语言特征的统计模型,将网页文档按文本行转化成正、负密度序列,再根据邻近行的内容连续性,利用高斯平滑技术修正文本密度序列,最后采用改进的最大子序列分割序列抽取正文内容。该方法保持正文完整性并排除噪声干扰,且无需人工干预或反复训练。实验结果表明基于文本密度抽取正文对不同数据源具有广泛的适应性,且准确率和召回率优于现有统计模型。
其他语种文摘	In order to obtain useful content encompassed by a large number of irrelevant information, the content extraction becomes indispensable for web data application. An approach of web content extraction based on the text density model is proposed, which integrates page structure features with language features to convert text lines of page document into a positive or negative density sequence. Additionally, the Gaussian smoothing technique is used to revise the density sequence, which takes the content continuity of adjacent lines into consideration. Finally, the improved maximum sequence segmentation is adopted to split the sequence and extract web content. Without any human intervention or repeated trainings, this approach maintains the integrity of content and eliminates noise disturbance. The experimental results indicate that the web content extraction based on the text density model is widely adapted to different data sources, and both accuracy and recall rate of the proposed approach are better than those existing statistical models.
来源	模式识别与人工智能 ,2013,26(7):667-672 【核心库】
关键词	Web挖掘 ; 正文抽取 ; 文本密度 ; 高斯平滑 ; 最大子序列
地址	1. 中国科学技术大学自动化系, 合肥, 230026 2. 中国科学院合肥智能机械研究所, 合肥, 230031
语种	中文
文献类型	研究性论文
ISSN	1003-6059
学科	自动化技术、计算机技术
基金	国家自然科学基金项目 ; 国家科技支撑计划项目
文献收藏号	CSCD:4901889