【摘要】 随着互联网的飞速发展,大量的文本信息被分享到网上,如何在海量的网络信息中提取出可靠性较高的人物关系已成为信息抽取领域中的一个重要研究课题。为深入进行人物关系识别任务在中文方面的研究,提出了基于多元特征的分块人物关系识别系统,设计了较为完备的特征池,包括词袋特征、相关频率特征、依存树(DT)特征、命名实体识别(NER)特征等,为不同的关系从特征池中选择效果最佳的特征集合,并实验了多种基于有监督的机器学习分类算法。本系统在2015年中国机器学习会议竞赛(CCML Competition)举办的两个任务(Task1是从单个新闻标题中判定给定人物的关系;Task2是从多个新闻标题中判定人物的关系)的数据集上分别取得了75.68%和76.58%的Macro F1值,均位列参赛成绩的第一名。更多还原

【Abstract】 With the rapid development of Internet,huge amount of textual information is accessible on the Internet. The task of reliable person-person relation extraction from Web page has become an import research topic in the field of information extraction. To address this problem,this work implemented a blocked person relation recognition system and adopted abundant of features,i. e.,bag-of-word,relevant frequency,Dependency Tree( DT),Named Entity Recognition( NER) features,etc. A series of experiments were conducted to select out optimal feature set and classification algorithm for each relation type to improve the performance. This system was performed on two tasks in China Conference of Machine Learn Competition( CCML Competition) of 2015,to recognize person relation from single or a set of news titles in Chinese( Task1 and Task2,respectively). For these two tasks,this system achieved the Macro F1 score of 75. 68% and 76. 58%,respectively and ranked the 1st on both tasks.更多还原