Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of
  Negations   Adverse Drug Event (ADE) extraction models can rapidly examine large
collections of social media texts, detecting mentions of drug-related adverse
reactions and trigger medical investigations. However, despite the recent
advances in NLP, it is currently unknown if such models are robust in face of
negation, which is pervasive across language varieties.
  In this paper we evaluate three state-of-the-art systems, showing their
fragility against negation, and then we introduce two possible strategies to
increase the robustness of these models: a pipeline approach, relying on a
specific component for negation detection; an augmentation of an ADE extraction
dataset to artificially create negated samples and further train the models.
  We show that both strategies bring significant increases in performance,
lowering the number of spurious entities predicted by the models. Our dataset
and code will be publicly released to encourage research on the topic.

13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn