Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks
Ilham Fathy Saputra, Rahmad Mahendra, Alfan Farizki Wicaksono
Abstract
We propose keyphrases extraction technique to extract important terms from the healthcare user-generated contents. We employ deep learning architecture, i.e. Long Short-Term Memory, and leverage word embeddings, medical concepts from a knowledge base, and linguistic components as our features. The proposed model achieves 61.37% F-1 score. Experimental results indicate that our proposed approach outperforms the baseline methods, i.e. RAKE and CRF, on the task of extracting keyphrases from Indonesian health forum posts.- Anthology ID:
- W18-2304
- Volume:
- Proceedings of the BioNLP 2018 workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 28–34
- Language:
- URL:
- https://aclanthology.org/W18-2304
- DOI:
- 10.18653/v1/W18-2304
- Cite (ACL):
- Ilham Fathy Saputra, Rahmad Mahendra, and Alfan Farizki Wicaksono. 2018. Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks. In Proceedings of the BioNLP 2018 workshop, pages 28–34, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks (Saputra et al., BioNLP 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-2304.pdf