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.

翻译：本文提出了一种新颖的方法来解决NLPCC 2015中的实体识别与链接挑战。该任务涉及从短搜索查询中提取命名实体提及，并将其链接至参考中文知识库中的实体。为解决此问题，我们首先扩展现有知识库并利用外部知识识别候选实体，从而提高召回率。接着，我们从候选实体中提取特征，并采用支持向量回归与多重加性回归树作为评分函数对结果进行过滤。此外，我们应用规则进一步优化结果以提升精确率。该方法计算效率高，最终获得了0.535的F1分数。