This paper presents a novel approach to address the Entity Recognition and
Linking Challenge at NLPCC 2015. The task involves extracting named entity
mentions from short search queries and linking them to entities within a
reference Chinese knowledge base. To tackle this problem, we first expand the
existing knowledge base and utilize external knowledge to identify candidate
entities, thereby improving the recall rate. Next, we extract features from the
candidate entities and utilize Support Vector Regression and Multiple Additive
Regression Tree as scoring functions to filter the results. Additionally, we
apply rules to further refine the results and enhance precision. Our method is
computationally efficient and achieves an F1 score of 0.535.

该研究论文提出了一种新方法来解决 NLPCC 2015 中的实体识别和链接挑战问题，通过扩展现有知识库并利用外部知识识别候选实体，提高了召回率，同时利用支持向量回归和多增加回归树作为评分函数来过滤结果，应用规则进一步优化结果并提高精确度，该方法计算效率高，F1 得分为 0.535。