A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities

Author:

Sarker Abeed,Gonzalez Graciela

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference20 articles.

1. NIH Grant Number 5R01LM011176, Mining Social Network Postings for Mentions of Potential Adverse Drug Reactions, 2012. Report URL: 〈https://projectreporter.nih.gov/project_info_description.cfm?Projectnumber=5R01LM011176-02〉.

2. Portable automatic text classification for adverse drug reaction detection via multi-corpus training;Sarker;J. Biomed. Inform.,2015

3. P. Pimpalkhute, A. Patki, A. Nikfarjam, Phonetic spelling filter for keyword selection in drug mention mining from social media, in: Proceedings of AMIA Summits on Translational Science Proceedings, 2014, 2014, pp. 90–95 (PMID: 25717407).

4. T. Mikolov, W. Yih, G. Zweig, Linguistic regularities in continuous space word representations, in: Proceedings of NAACL-HLT, 9–14 June 2013, Association for Computational Linguistics, Atlanta, Georgia, 2013, pp. 746–751.

5. Identifying adverse drug event information in clinical notes with distributional semantic representations of context;Henriksson;J. Biomed. Inform.,2015

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