BLINK与Elasticsearch在商业对话中有效的实体链接

Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, TN ShashiBhushan, Simon Corston-Oliver
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引用次数: 14

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,在生产环境中部署用于高效实时推理的神经实体链接系统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,该系统将业务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。本文提出的系统利用Elasticsearch来保证在资源有限的云机器上部署时的推理效率,在保持高精度的同时,在推理速度和内存消耗方面得到了显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
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.
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