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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBioNLP 2018 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 17th BioNLP Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages28-34
Number of pages7
ISBN (Electronic)9781948087339
Publication statusPublished - 2018
Event17th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2018 - Melbourne, Australia
Duration: 19 Jul 2018 → …

Publication series

NameBioNLP 2018 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 17th BioNLP Workshop

Conference

Conference17th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2018
Country/TerritoryAustralia
CityMelbourne
Period19/07/18 → …

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