May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2020.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 2.
5 papers.
- 2025A Survey on Current Trends and Recent Advances in Text Anonymizationcited by 5
- 2022EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identificationcited by 2
- 2023E2E Spoken Entity Extraction for Virtual Agentscited by 0
- 2023Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Inputcited by 0
- 2025Call2Instruct: Automated Pipeline for Generating Q&A Datasets from Call Center Recordings for LLM Fine-Tuningcited by 0
- 2025Call2Instruct: Automated Pipeline for Generating Q&A Datasets from Call Center Recordings for LLM Fine-Tuning1 Dec 2025
- 2025A Survey on Current Trends and Recent Advances in Text Anonymization29 Aug 2025
- 2023Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input21 Oct 2023
- 2023E2E Spoken Entity Extraction for Virtual Agents16 Feb 2023
- 2022EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification28 Apr 2022