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.
CITATION STYLE
Farmanbar, M., van Ommeren, N., & Zhao, B. (2020). Semantic search with domain-specific word-embedding and production monitoring in Fintech. In COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of System Demonstrations (pp. 28–33). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.coling-demos.6
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