Abstract
The CLEF eHealth 2016 Task 1 is set to automatically assign pre-defined medical tag to each word in the patient case records. The dificulty of the task is that many classes have little training data. This paper presents our work on the 2016 CLEF eHealth Task 1. In particular, we propose an optimized Conditional Random Field algorithm to better fulfill the task. We also utilize the information extracted through association rules and MetaMap to boost the performance of our results. The evaluation results show our runs outperform the four oficial baselines in this dificulty task.
| Original language | English |
|---|---|
| Pages (from-to) | 147-156 |
| Number of pages | 10 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1609 |
| State | Published - 2016 |
| Event | 2016 Working Notes of Conference and Labs of the Evaluation Forum, CLEF 2016 - Evora, Portugal Duration: 5 Sep 2016 → 8 Sep 2016 |
Keywords
- Association Rule
- Conditional Random Field
- Information Extraction
- MetaMap