Name
Title | ||
|---|---|---|
Understanding Medical Conversations With Scattered Keyword Attention And Weak Supervision From Responses |
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. |
Year | Venue | DocType |
|---|---|---|
2020 | national conference on artificial intelligence | Conference |
Volume | ISSN | Citations |
34 | 2159-5399 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Shi | 1 | 0 | 0.34 |
| Haifeng Hu | 2 | 0 | 0.34 |
| Wanxiang Che | 3 | 711 | 66.39 |
| Zhongqian Sun | 4 | 0 | 0.68 |
| Ting Liu | 5 | 2735 | 232.31 |
| Junzhou Huang | 6 | 2182 | 141.43 |