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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.wnut-1.1.pdf
- Data
- CoNLL++, CoNLL-2003