BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
Computation and Language
2022-05-10 v1 Information Retrieval
Machine Learning
Abstract
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.
Cite
@article{arxiv.2205.04438,
title = {BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations},
author = {Md Tahmid Rahman Laskar and Cheng Chen and Aliaksandr Martsinovich and Jonathan Johnston and Xue-Yong Fu and Shashi Bhushan TN and Simon Corston-Oliver},
journal= {arXiv preprint arXiv:2205.04438},
year = {2022}
}
Comments
NAACL 2022