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
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–34
Language:
URL:
https://aclanthology.org/W18-2304
DOI:
10.18653/v1/W18-2304
Bibkey:
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., 2018)
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PDF:
https://aclanthology.org/W18-2304.pdf