Natural language understanding for task oriented dialog in the biomedical domain in a low ressources context, NIPS Workshop - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Natural language understanding for task oriented dialog in the biomedical domain in a low ressources context, NIPS Workshop

Leonardo Campillos Llanos
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  • PersonId : 1022476
Anita Burgun
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  • PersonId : 1034092

Résumé

In the biomedical domain, the lack of sharable datasets often limit the possibility of developing natural language processing systems, especially dialogue applica- tions and natural language understanding models. To overcome this issue, we explore data generation using templates and terminologies and data augmentation approaches. Namely, we report our experiments using paraphrasing and word representations learned on a large EHR corpus with Fasttext and ELMo, to learn a NLU model without any available dataset. We evaluate on a NLU task of natural language queries in EHRs divided in slot-filling and intent classification sub-tasks. On the slot-filling task, we obtain a F-score of 0.76 with the ELMo representation; and on the classification task, a mean F-score of 0.71. Our results show that this method could be used to develop a baseline system.
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Dates et versions

hal-02358037 , version 1 (11-11-2019)

Identifiants

  • HAL Id : hal-02358037 , version 1

Citer

Antoine Neuraz, Leonardo Campillos Llanos, Anita Burgun, Sophie Rosset. Natural language understanding for task oriented dialog in the biomedical domain in a low ressources context, NIPS Workshop. Machine Learning for Health, Dec 2018, Montréal, Québec, Canada. ⟨hal-02358037⟩
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