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

Authors

  • Xiaoming Shi Harbin Institute of Technology
  • Haifeng Hu Tencent
  • Wanxiang Che Harbin Institute of Technology
  • Zhongqian Sun Tencent
  • Ting Liu Harbin Institute of Technology
  • Junzhou Huang Tencent

DOI:

https://doi.org/10.1609/aaai.v34i05.6412

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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Published

2020-04-03

How to Cite

Shi, X., Hu, H., Che, W., Sun, Z., Liu, T., & Huang, J. (2020). Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8838-8845. https://doi.org/10.1609/aaai.v34i05.6412

Issue

Section

AAAI Technical Track: Natural Language Processing