基于多源知识的中文微博命名实体链接

命名实体在文本中是承载信息的重要单元,而微博作为一种分享简短实时信息的社交网络平台,其文本长度短、不规范,而且常有新词出现,这就需要对其命名实体进行准确的理解,以提高对文本信息的正确分析。提出了基于多源知识的中文微博命名实体链接,把同义词词典、百科资源等知识与词袋模型相结合实现命名实体的链接。在NLP&CC2013中文微博实体链接评测数据集进行了实验,获得微平均准确率为92.97%,与NLP&CC2013中文实体链接评测最好的评测结果相比,提高了两个百分点。

Chinese Micro-blog named entity linking based on multisource knowledge

Named entity is an important component conveying information in texts. Micro-blog is a social network platform used to share brief real-time information, with characteristics such as short text length, nonstandard words, and even the frequent emergence of neologisms.So an accurate understanding of the named entities is needed to ensure a correct analysis of the text information. A Chinese Micro-blog entity linking strategy was proposed based on multi-resource knowledge, combing the dictionary of synonyms, the encyclopedia resources as well as the bag-of-words model together to deal with named entity linking.In this strategy, named entities to be linked in Micro-blog were mapped to the corresponding candidate entities in the knowledge base. The evaluation results obtain a micro average accuracy of 92.97%, based on experiments using data sets of NLP& CC2013 Chinese micro-blog entity linking track. Compared with the state-of-the-art result, the accuracy of this method is two percent higher,which demonstrates the effectiveness of our method.