BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan Tn, Simon Corston-Oliver
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.
BibTeX
@inproceedings{laskar-etal-2022-blink,
title = "{BLINK} with {E}lasticsearch for Efficient Entity Linking in Business Conversations",
author = "Laskar, Md Tahmid Rahman and
Chen, Cheng and
Martsinovich, Aliaksandr and
Johnston, Jonathan and
Fu, Xue-Yong and
Tn, Shashi Bhushan and
Corston-Oliver, Simon",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.38/",
doi = "10.18653/v1/2022.naacl-industry.38",
pages = "344--352"
}