The paper gives a brief overview of three shared tasks which have been organized at the
PAN 2023 lab on digital text forensics and stylometry hosted at the CLEF 2023 conference …

With this work we propose an application of the ELECTRA Transformer, fine-tuned on two
augmented version of the same training dataset. Our team developed the novel framework …

In this work we propose an application of XLNet to address the task hosted at PAN@
CLEF2023 related to Profiling Cryptocurrency Influencers with Few-shot Learning. For our …

From a few-shot learning perspective, we propose a strategy to enrich the latent semantic of
the text provided in the dataset provided for the Profiling Cryptocurrency Influencers with …

Abstract Few Shot Learning (FSL) is a supervised Machine Learning (ML) problem which
deals with learning with few labeled samples. To address the challenges of FSL in terms of …

In recent years, the rise of influential individuals in cryptocurrencies on social media has
played a significant role both on the internet and in investments. One of the major …

In these working notes, we present our contributions to the “Profiling Cryptocurrency
Influencers with Few-shot Learning” shared task at PAN 2023 under the team name pan23 …

The objective of author profiling (AP) is to study the characteristics of authors through the
analysis of how language is exchanged among people. Studying these attributes sometimes …

In this paper, we describe our systems for participating in the “Profiling Cryptocurrency
Influencers with Few-shot Learning” task shared on PAN 2023. This work focuses on …

We proposed a novel approach that leverages embedding augmentation based on
contrastive learning to address the issue of few-shot learning in the task of analyzing …