# May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance

arXiv: https://arxiv.org/abs/2010.15598 · HTML: https://mlsanity.com/paper/2010.15598/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification](https://mlsanity.com/paper/2204.13496/) (2022)
- [E2E Spoken Entity Extraction for Virtual Agents](https://mlsanity.com/paper/2302.10186/) (2023)
- [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 investigate using Named Entity Recognition on a new type of user-generated text: a call center conversation. These conversations combine problems from spontaneous speech with problems novel to conversational Automated Speech Recognition, including incorrect recognition, alongside other common problems from noisy user-generated text. Using our own corpus with new annotations, training custom contextual string embeddings, and applying a BiLSTM-CRF, we match state-of-the-art results on our novel task.