# BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations

arXiv: https://arxiv.org/abs/2205.04438 · HTML: https://mlsanity.com/paper/2205.04438/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models](https://mlsanity.com/paper/2109.05620/) (2021)
- [WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2011.01421/) (2020)
- [Falcon 2.0: An Entity and Relation Linking Tool over Wikidata](https://mlsanity.com/paper/1912.11270/) (2019)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
- [Interpretable Entity Representations through Large-Scale Typing](https://mlsanity.com/paper/2005.00147/) (2020)
- [CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata](https://mlsanity.com/paper/2101.09969/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)

## Built on by (most cited first, 13 of 13)

- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks](https://mlsanity.com/paper/2310.04270/) (2023)
- [Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective](https://mlsanity.com/paper/2310.19233/) (2023)
- [ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries](https://mlsanity.com/paper/2304.13620/) (2023)
- [Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs](https://mlsanity.com/paper/2311.00681/) (2023)
- [Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers](https://mlsanity.com/paper/2306.04504/) (2023)
- [AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching](https://mlsanity.com/paper/2305.17619/) (2023)
- [An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts](https://mlsanity.com/paper/2209.13736/) (2022)
- [Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop](https://mlsanity.com/paper/2211.01354/) (2022)
- [CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia](https://mlsanity.com/paper/2305.06147/) (2023)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Entity-level Sentiment Analysis in Contact Center Telephone Conversations](https://mlsanity.com/paper/2210.13401/) (2022)
- [CoRE-CoG: Conversational Recommendation of Entities using Constrained Generation](https://mlsanity.com/paper/2311.08511/) (2023)

## Abstract (arXiv)

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.