# An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts

arXiv: https://arxiv.org/abs/2209.13736 · HTML: https://mlsanity.com/paper/2209.13736/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [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)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Distilling Task-Specific Knowledge from BERT into Simple Neural Networks](https://mlsanity.com/paper/1903.12136/) (2019)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
- [ner and pos when nothing is capitalized](https://mlsanity.com/paper/1903.11222/) (2019)
- [BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations](https://mlsanity.com/paper/2205.04438/) (2022)
- [Improving Punctuation Restoration for Speech Transcripts via External Data](https://mlsanity.com/paper/2110.00560/) (2021)
- [Developing a Production System for Purpose of Call Detection in Business Phone Conversations](https://mlsanity.com/paper/2205.06904/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching](https://mlsanity.com/paper/2305.17619/) (2023)
- [Improving Named Entity Recognition in Telephone Conversations via Effective Active Learning with Human in the Loop](https://mlsanity.com/paper/2211.01354/) (2022)
- [AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents](https://mlsanity.com/paper/2510.08149/) (2025)
- [Entity-level Sentiment Analysis in Contact Center Telephone Conversations](https://mlsanity.com/paper/2210.13401/) (2022)
- [Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input](https://mlsanity.com/paper/2310.14110/) (2023)
- [Call2Instruct: Automated Pipeline for Generating Q&A Datasets from Call Center Recordings for LLM Fine-Tuning](https://mlsanity.com/paper/2601.14263/) (2025)

## Abstract (arXiv)

We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition. We first fine-tune LUKE, a state-of-the-art Named Entity Recognition (NER) model, on a limited amount of transcripts, then use it as the teacher model to teach a smaller DistilBERT-based student model using a large amount of weakly labeled data and a small amount of human-annotated data. The model achieves high accuracy while also satisfying the practical constraints for inclusion in a commercial telephony product: realtime performance when deployed on cost-effective CPUs rather than GPUs.