UArizona at the MADE1.0 NLP Challenge

Dongfang Xu, Vikas Yadav, Steven Bethard

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations


MADE1.0 is a public natural language processing challenge aiming to extract medication and adverse drug events from Electronic Health Records. This work presents NER and RI systems developed by UArizona team for the MADE1.0 competition. We propose a neural NER system for medical named entity recognition using both local and context features for each individual word and a simple but effective SVM-based pairwise relation classification system for identifying relations between medical entities and attributes. Our system achieves 81.56%, 83.18%, and 59.85% F1 score in the three tasks of MADE1.0 challenge, respectively, ranked amongst the top three teams for Task 2 and 3.

Original languageEnglish (US)
Pages (from-to)57-65
Number of pages9
JournalProceedings of Machine Learning Research
StatePublished - 2018
Event1st International Workshop on Medication and Adverse Drug Event Detection, MADE 2018 -
Duration: May 4 2018 → …


  • Adverse Drug Event
  • Information Extraction
  • Neural Network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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