On the accuracy of different neural language model approaches to ADE extraction in natural language corpora

摘要

The problem of extracting mentions of adverse events and reactions from text is especially relevant nowadays due to rapid emergence of datasets including such events, and progress in text analysis tools. This paper presents a comparison of existing methods for the task of automated extraction of adverse events from natural language texts. The considered methods are based on neural-network language models, pre-trained on different sets of unlabeled data. Experiments have been performed on the n2c2-2018 and CADEC corpora, using metrics coined within the CoNLL competition. Models of the aforementioned type show efficient solution of this task, provided sufficient amount of labeled training samples during.