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

Micaela Kaplan


Abstract
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.
Anthology ID:
2020.wnut-1.1
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2020.wnut-1.1
DOI:
10.18653/v1/2020.wnut-1.1
Bibkey:
Cite (ACL):
Micaela Kaplan. 2020. May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 1–6, Online. Association for Computational Linguistics.
Cite (Informal):
May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance (Kaplan, WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.1.pdf
Data
CoNLL 2003CoNLL++