Abstract
We present an end-to-end information retrieval system with domain-specific custom language models for accurate search terms expansion. The text mining pipeline tackles several challenges faced in an industry-setting, including multi-lingual jargon-rich unstructured text and privacy compliance. Combined with a novel statistical approach for word embedding evaluations, the models can be monitored in a production setting. Our approach is used in the real world in risk management in the financial sector and has wide applicability to other domains.- Anthology ID:
- 2020.coling-demos.6
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Michal Ptaszynski, Bartosz Ziolko
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics (ICCL)
- Note:
- Pages:
- 28–33
- Language:
- URL:
- https://aclanthology.org/2020.coling-demos.6
- DOI:
- 10.18653/v1/2020.coling-demos.6
- Cite (ACL):
- Mojtaba Farmanbar, Nikki Van Ommeren, and Boyang Zhao. 2020. Semantic search with domain-specific word-embedding and production monitoring in Fintech. In Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, pages 28–33, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).
- Cite (Informal):
- Semantic search with domain-specific word-embedding and production monitoring in Fintech (Farmanbar et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.coling-demos.6.pdf