Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses

Xiaoming Shi, Haifeng Hu, Wanxiang Che, Zhongqian Sun, Ting Liu, Junzhou Huang

AAAI 2020 pp. 8838-8845

doi:10.1609/AAAI.V34I05.6412 /aaai/2020/shi2020aaai-understanding/

Abstract

In this work, we consider the medical slot filling problem, i.e., the problem of converting medical queries into structured representations which is a challenging task. We analyze the effectiveness of two points: scattered keywords in user utterances and weak supervision with responses. We approach the medical slot filling as a multi-label classification problem with label-embedding attentive model to pay more attention to scattered medical keywords and learn the classification models by weak-supervision from responses. To evaluate the approaches, we annotate a medical slot filling data and collect a large scale unlabeled data. The experiments demonstrate that these two points are promising to improve the task.

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Cite

Text

Shi et al. "Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I05.6412

Markdown

[Shi et al. "Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/shi2020aaai-understanding/) doi:10.1609/AAAI.V34I05.6412

BibTeX

@inproceedings{shi2020aaai-understanding,
  title     = {{Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses}},
  author    = {Shi, Xiaoming and Hu, Haifeng and Che, Wanxiang and Sun, Zhongqian and Liu, Ting and Huang, Junzhou},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {8838-8845},
  doi       = {10.1609/AAAI.V34I05.6412},
  url       = {https://mlanthology.org/aaai/2020/shi2020aaai-understanding/}
}