论文标题
与Elasticsearch眨眼，以链接业务对话中的有效实体
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
论文作者
论文摘要
一个实体链接系统将文本中实体的文本提及与知识库中的相应条目保持一致。但是，部署神经实体链接系统以在生产环境中有效的实时推断是一项艰巨的任务。在这项工作中，我们提出了一个神经实体链接系统，该系统将业务对话中的产品和组织类型实体连接到其相应的Wikipedia和Wikidata条目。提出的系统利用Elasticsearch在资源有限的云机中确保推理效率，并在推理速度和内存消耗方面取得了重大改进，同时保持高精度。
An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for efficient real-time inference in production environments is a challenging task. In this work, we present a neural entity linking system that connects the product and organization type entities in business conversations to their corresponding Wikipedia and Wikidata entries. The proposed system leverages Elasticsearch to ensure inference efficiency when deployed in a resource limited cloud machine, and obtains significant improvements in terms of inference speed and memory consumption while retaining high accuracy.
