An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations

Abstract

An entity linking system links named entities in a text to their corresponding entries in a knowledge base. In recent years, building an entity linking system that leverages the transformer architecture has gained lots of attention. However, deploying a transformer-based neural entity linking system in industrial production environments in a limited resource setting is a challenging task. In this work, we present an entity linking system that leverages a transformer-based BERT encoder (the BLINK model) to connect the product and organization type entities in business phone conversations to their corresponding Wikipedia entries. We propose a dimensionality reduction technique via utilizing an auto encoder that can effectively compress the dimension of the pre-trained BERT embeddings to 256 from the original size of 1024. This allows our entity linking system to significantly optimize the space requirement when deployed in a resource limited cloud machine while reducing the inference time along with retaining high accuracy.