Medical Spoken Named Entity Recognition

Khai Le-Duc; David Thulke; Hung-Phong Tran; Long Vo-Dang; Khai-Nguyen Nguyen; Truong-Son Hy; Ralf Schlüter

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arXiv · 2406.13337

Medical Spoken Named Entity Recognition

Khai Le-Duc·David Thulke·Hung-Phong Tran·Long Vo-Dang·Khai-Nguyen Nguyen·Truong-Son Hy·Ralf Schlüter

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Abstract

Spoken Named Entity Recognition (NER) aims to extract named entities from speech and categorise them into types like person, location, organization, etc. In this work, we present VietMed-NER - the first spoken NER dataset in the medical domain. To our knowledge, our Vietnamese real-world dataset is the largest spoken NER dataset in the world regarding the number of entity types, featuring 18 distinct types. Furthermore, we present baseline results using various state-of-the-art pre-trained models: encoder-only and sequence-to-sequence; and conduct quantitative and qualitative error analysis. We found that pre-trained multilingual models generally outperform monolingual models on reference text and ASR output and encoders outperform sequence-to-sequence models in NER tasks. By translating the transcripts, the dataset can also be utilised for text NER in the medical domain in other languages than Vietnamese. All code, data and models are publicly available: https://github.com/leduckhai/MultiMed/tree/master/VietMed-NER.

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BibTeX RIS

Khai Le-Duc, David Thulke, Hung-Phong Tran, Long Vo-Dang, Khai-Nguyen Nguyen, Truong-Son Hy, Ralf Schlüter. 2024-06-19. Medical Spoken Named Entity Recognition. https://arxiv.org/abs/2406.13337

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