… Our text classification approach relies on … text nuggets. Importantly, using our expanded
feature sets, we combine training data from different corpora in attempts to boost classification …

… We review text mining methods used to process unstructured data sources such as
medical literature and social media in this section. One important aspect of working with …

… matching ADRs in texts. Although the … detecting mentions of drug names and adverse
reactions, we consider it to be beyond the scope of this paper, which focuses on a signal detection …

… This study would make the possibility of further investigations into their adverse effects on
the various cell through text mining and summarization techniques for evaluation of the …

… To address the above issues, we formulate ADR detection as a text classification task and
introduce an adversarial transfer learning framework into ADR detection. Our method focuses …

… of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as
binary classification… Portable automatic text classification for adverse drug reaction detection …

… Given a tweet, participants of this shared task are required to detect whether … text classification
models that combine textual and drug modalities. More formally, consider we have a text …

… mentions of drugs and symptoms to facilitate detection of ADE mentions in clinical text. … to
tackle the problem of accurate detection of adverse drug events in clinical narrative. Detailed …

… Adverse drug reactions (ADRs) are a major public health … Clinical drug trials have limited
ability to detect all ADRs due to … to perform very well in text classification problems. We used …

… a benchmark text mining application (classification of tweets … Classification was performed
using two classes of machine … and identification techniques for adverse drug reactions in …