基于多源知识和Ranking SVM的中文微博命名实体链接

命名实体是文本中承载信息的重要单元,正确分析存在歧义的命名实体对文本的理解起着关键性作用。该文提出基于多源知识和Ranking SVM的中文微博命名实体链接,结合同义词词典、百科资源等知识产生初始候选实体集合,同时从文本中抽取多种组合特征,利用Ranking SVM对候选实体集合进行排序,从而得到目标实体。在NLP&CC2014^①中文微博实体链接评测数据集上进行了实验,获得了89.40%的平均准确率,与NLP&CC2014中文微博实体链接评测取得最好成绩的系统相比,本文的系统具有一定的优势。

Chinese Micro-blog Named Entity Linking Based on Multisource Knowledge

Named entity is an important component conveying information in texts, and an accurate understanding of named entities is necessary to ensure a correct analysis of the text information. This paper proposes a Chinese micro-blog entity linking strategy based on multi-resource knowledge under Ranking SVM framework. It combines a dictionary of synonyms, the encyclopedia resources to produce an initial set of candidate entities , then extracts various combinations of featuresfor Ranking SVM to generate the target entity set. The evaluation on data sets of NLP&CC2014 Chinese micro-blog entity linking track shows a micro average accuracy of 89.40%, which is better than the state-of-the-art result.