DeepAI AI Chat
Log In Sign Up

BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations

by   Md Tahmid Rahman Laskar, et al.

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.


page 1

page 2

page 3

page 4


NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker

Entity Linking (EL) is the task of detecting mentions of entities in tex...

Entity Linking and Discovery via Arborescence-based Supervised Clustering

Previous work has shown promising results in performing entity linking b...

Neural Entity Linking on Technical Service Tickets

Entity linking, the task of mapping textual mentions to known entities, ...

Joint Coreference Resolution and Character Linking for Multiparty Conversation

Character linking, the task of linking mentioned people in conversations...

Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty

Entity Linking (EL) is the task of automatically identifying entity ment...

Pangloss: Fast Entity Linking in Noisy Text Environments

Entity linking is the task of mapping potentially ambiguous terms in tex...

Improving Entity Linking by Modeling Latent Relations between Mentions

Entity linking involves aligning textual mentions of named entities to t...