Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks

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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.

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APA

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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