Search Results for author: Beatrice Portelli

Found 11 papers, 6 papers with code

Keyphrase Generation with GANs in Low-Resources Scenarios

no code implementations EMNLP (sustainlp) 2020 Giuseppe Lancioni, Saida S.Mohamed, Beatrice Portelli, Giuseppe Serra, Carlo Tasso

Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document.

Keyphrase Generation

Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation

no code implementations6 Sep 2022 Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra

In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.

NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations

1 code implementation WNUT (ACL) 2021 Simone Scaboro, Beatrice Portelli, Emmanuele Chersoni, Enrico Santus, Giuseppe Serra

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.

Negation Detection

Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19

1 code implementation25 Jul 2021 Kevin Roitero, Michael Soprano, Beatrice Portelli, Massimiliano De Luise, Damiano Spina, Vincenzo Della Mea, Giuseppe Serra, Stefano Mizzaro, Gianluca Demartini

Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.

Misinformation

The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?

1 code implementation13 Aug 2020 Kevin Roitero, Michael Soprano, Beatrice Portelli, Damiano Spina, Vincenzo Della Mea, Giuseppe Serra, Stefano Mizzaro, Gianluca Demartini

Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.

Misinformation

Distilling the Evidence to Augment Fact Verification Models

no code implementations WS 2020 Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra, Enrico Santus

We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim.

Fact Verification

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