Publications by authors named "Felice Dell'Orletta"

In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Large Language Models (LLMs) within the biomedical domain, a field that poses unique challenges due to its complexity and the specialized nature of its data. Building on the foundation laid by the transformative architecture of Transformers, we investigate the nuanced dynamics of LLMs through a multifaceted lens, focusing on two domain-specific tasks, i.e.

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Introduction: Language is usually considered the social vehicle of thought in intersubjective communications. However, the relationship between language and high-order cognition seems to evade this canonical and unidirectional description (ie, the notion of language as a simple means of thought communication). In recent years, clinical high at-risk mental state (CHARMS) criteria (evolved from the Ultra-High-Risk paradigm) and the introduction of the Clinical Staging system have been proposed to address the dynamicity of early psychopathology.

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The act of lying and its detection have raised interest in many fields, from the legal system to our daily lives. Considering that testimonies are commonly based on linguistic parameters, natural language processing, a research field concerned with programming computers to process and analyse natural language texts or speech, is a topic of interest on this front. This study aimed to examine the linguistic styles of simulated deception and true testimonies collected with the aim of studying witness memory.

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Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years.

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In this paper, we present an overview of existing parallel corpora for Automatic Text Simplification (ATS) in different languages focusing on the approach adopted for their construction. We make the main distinction between manual and (semi)-automatic approaches in order to investigate in which respect complex and simple texts vary and whether and how the observed modifications may depend on the underlying approach. To this end, we perform a two-level comparison on Italian corpora, since this is the only language, with the exception of English, for which there are large parallel resources derived through the two approaches considered.

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Introduction: The term pro-ana (pro-anorexia) means the spread of restrictive eating behaviors and anorectic advices in virtual spaces written by teenagers. The purpose of this pilot study consists in a qualitative and quantitative analysis of foods contained in a linguistic corpus made up of users' comments on pro-ana websites.

Method: The corpus of pro-ana websites was analyzed through the T2K tool based on word-frequency processing.

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Background: Cancer cells are characterized by chromosomal instability (CIN) and it is thought that errors in pathways involved in faithful chromosome segregation play a pivotal role in the genesis of CIN. Cohesin forms a large protein ring that binds DNA strands by encircling them. In addition to this central role in chromosome segregation, cohesin is also needed for DNA repair, gene transcription regulation and chromatin architecture.

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