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.
CITATION STYLE
Saputra, I. F., Mahendra, R., & Wicaksono, A. F. (2018). Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks. In BioNLP 2018 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 17th BioNLP Workshop (pp. 28–34). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w18-2304
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