Medical Spoken Named Entity Recognition
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913770896359424 |
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| author | Le-Duc, Khai Thulke, David Tran, Hung-Phong Vo-Dang, Long Nguyen, Khai-Nguyen Hy, Truong-Son Schlüter, Ralf |
| author_facet | Le-Duc, Khai Thulke, David Tran, Hung-Phong Vo-Dang, Long Nguyen, Khai-Nguyen Hy, Truong-Son Schlüter, Ralf |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_13337 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Medical Spoken Named Entity Recognition Le-Duc, Khai Thulke, David Tran, Hung-Phong Vo-Dang, Long Nguyen, Khai-Nguyen Hy, Truong-Son Schlüter, Ralf Audio and Speech Processing Computation and Language Machine Learning Sound 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. |
| title | Medical Spoken Named Entity Recognition |
| topic | Audio and Speech Processing Computation and Language Machine Learning Sound |
| url | https://arxiv.org/abs/2406.13337 |