Name
Abstract | ||
|---|---|---|
Adverse Drug Event (ADE) extraction mod-els can rapidly examine large collections of so-cial media texts, detecting mentions of drug-related adverse reactions and trigger medicalinvestigations. However, despite the recent ad-vances in NLP, it is currently unknown if suchmodels are robust in face ofnegation, which ispervasive across language varieties.In this paper we evaluate three state-of-the-artsystems, showing their fragility against nega-tion, and then we introduce two possible strate-gies to increase the robustness of these mod-els: a pipeline approach, relying on a specificcomponent for negation detection; an augmen-tation of an ADE extraction dataset to artifi-cially create negated samples and further trainthe models.We show that both strategies bring significantincreases in performance, lowering the num-ber of spurious entities predicted by the mod-els. Our dataset and code will be publicly re-leased to encourage research on the topic. |
Year | Venue | DocType |
|---|---|---|
2021 | W-NUT | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Scaboro | 1 | 0 | 0.68 |
| Beatrice Portelli | 2 | 0 | 1.01 |
| Emmanuele Chersoni | 3 | 0 | 2.37 |
| Enrico Santus | 4 | 58 | 7.81 |
| Giuseppe Serra | 5 | 0 | 1.01 |