BLINK与Elasticsearch在业务对话中实现高效实体链接
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
摘要：
实体链接系统将文本中实体的文本提及与知识库中相应的条目对齐。然而，在生产环境中部署神经实体链接系统以进行有效的实时推理是一项具有挑战性的任务。在这项工作中，我们提出了一个神经实体链接系统，将商业对话中的产品和组织类型实体连接到相应的维基百科和维基数据条目。所提出的系统利用Elasticsearch来确保部署在资源有限的云机器中时的推理效率，并在推理速度和内存消耗方面获得显着改善，同时保持高准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
