Papers by Markus Zlabinger

2 papers
Medical Entity Corpus with PICO elements and Sentiment Analysis (L18-1)

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Challenge: In this paper, we establish a PICO and a sentiment annotated corpus of clinical trial publications.
Approach: They propose to create a phrase-level PICO corpus and a sentence-level sentiment annotated corpus from clinical trial publications.
Outcome: The proposed corpus is annotated on a phrase-level and a sentiment annotation on the same corpus.
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports (2020.findings-emnlp)

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Challenge: Evidence Based medicine searches for Participants, Interventions, and Outcomes (PIO) in clinical trial reports requires high-quality corpora.
Approach: They propose to use a crowd-annotated approach to search for PIOs in clinical trial reports to compensate for the lack of domain-specific expertise of crowdworkers by using similar sentences already annotated by experts.
Outcome: The proposed approach outperforms the Baseline approach and the crowd-annotated approach with similar sentences for each task-instance example.

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