【摘要】 随着电子计算机技术和互联网的快速发展,网络知识资源呈爆炸式增长,人们往往不能有效地获取、利用所需的网络知识资源。为了更好地利用网络知识资源,需要应用自动化、智能化的数据挖掘、信息提取方法。Web文档作为网络知识资源的一种载体,有着自然语言非结构化的特点,所以在运用聚类、分类等挖掘技术进行文本挖掘之前,需要将Web文档转化为机器学习算法可以理解的格式,即将文本数据转换成数值数据。针对现有常用文本表示方法的局限性,本文提出了一种基于命名实体和词向量相结合的网络知识资源表示学习模型。并在算法知识领域内进行实现与应用探索,包括网络解题报告的聚类和对网络解题报告的搜索,实验结果显示本文提出的方法在这些任务上取得了较好的效果。更多还原

【Abstract】 With the rapid development of computer technology and the Internet,the network knowledge resources are increasing,people often can not effectively access and use the network knowledge resources. In order to make better use of the network knowledge resources,the application of automation and intelligent data mining and information extraction methods are needed. As a carrier of knowledge resource on Web,Web document was non structured natural language,so before in using clustering and classification mining technology to text mining,the web document is required to be transformed into the format which can be understood for machine learning algorithms,that is to realize the conversion text data into numerical data. In view of the limitations of the existing common text representation methods,this paper proposes a network knowledge resource representation learning model based on the combination of named entity and word vector. And the paper discusses the implementation and application in the field of algorithm of knowledge,including clustering network solving report and search for network problem solving report. The experimental results show that method presented in this paper on these tasks achieved good results.更多还原