arXiv subjects

Filippo Chiarello

Publications and source records attributed to Filippo Chiarello.

5 recordsLinked to original sources

arXiv2024

Automating Customer Needs Analysis: A Comparative Study of Large Language Models in the Travel Industry

In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor and Reddit posts. Leveraging a diverse range of models, including both open-source and proprietary ones such as GPT-4 and Gemini, we aim to elucidate their strengths and weaknesses in this specialized domain. Through an evaluation process involving metrics such as BERTScore, ROUGE, and BLEU, we assess the performance of each model in accurately identifying and summarizing customer needs. Our findings highlight the efficacy of opensource LLMs, particularly Mistral 7B, in achieving comparable performance to larger closed models while offering affordability and customization benefits. Additionally, we underscore the importance of considering factors such as model size, resource requirements, and performance metrics when selecting the most suitable LLM for customer needs analysis tasks. Overall, this study contributes valuable insights for businesses seeking to leverage advanced NLP techniques to enhance customer experience and drive operational efficiency in the travel industry.

cs.CL↗

arXiv2021

SkillNER: Mining and Mapping Soft Skills from any Text

In today's digital world, there is an increasing focus on soft skills. On the one hand, they facilitate innovation at companies, but on the other, they are unlikely to be automated soon. Researchers struggle with accurately approaching quantitatively the study of soft skills due to the lack of data-driven methods to retrieve them. This limits the possibility for psychologists and HR managers to understand the relation between humans and digitalisation. This paper presents SkillNER, a novel data-driven method for automatically extracting soft skills from text. It is a named entity recognition (NER) system trained with a support vector machine (SVM) on a corpus of more than 5000 scientific papers. We developed this system by measuring the performance of our approach against different training models and validating the results together with a team of psychologists. Finally, SkillNER was tested in a real-world case study using the job descriptions of ESCO (European Skill/Competence Qualification and Occupation) as textual source. The system enabled the detection of communities of job profiles based on their shared soft skills and communities of soft skills based on their shared job profiles. This case study demonstrates that the tool can automatically retrieve soft skills from a large corpus in an efficient way, proving useful for firms, institutions, and workers. The tool is open and available online to foster quantitative methods for the study of soft skills.

cs.CL↗

arXiv2021

Defining definition: a Text mining Approach to Define Innovative Technological Fields

One of the first task of an innovative project is delineating the scope of the project itself or of the product/service to be developed. A wrong scope definition can determine (in the worst case) project failure. A good scope definition become even more relevant in technological intensive innovation projects, nowadays characterized by a highly dynamic multidisciplinary, turbulent and uncertain environment. In these cases, the boundaries of the project are not easily detectable and it is difficult to decide what it is in-scope and out-of-scope. The present work proposes a tool for the scope delineation process, that automatically define an innovative technological field or a new technology. The tool is based on Text Mining algorithm that exploits Elsevier's Scopus abstracts in order to the extract relevant data to define a technological scope. The automatic definition tool is then applied on four case studies: Artificial Intelligence and Data Science. The results show how the tool can provide many crucial information in the definition process of a technological field. In particular for the target technological field (or technology), it provides the definition and other elements related to the target.

cs.IR↗

arXiv2021

Rapid detection of fast innovation under the pressure of COVID-19

Covid-19 has rapidly redefined the agenda of technological research and development both for academics and practitioners. If the medical scientific publication system has promptly reacted to this new situation, other domains, particularly in new technologies, struggle to map what is happening in their contexts. The pandemic has created the need for a rapid detection of technological convergence phenomena, but at the same time it has made clear that this task is impossible on the basis of traditional patent and publication indicators. This paper presents a novel methodology to perform a rapid detection of the fast technological convergence phenomenon that is occurring under the pressure of the Covid-19 pandemic. The fast detection has been performed thanks to the use of a novel source: the online blogging platform Medium. We demonstrate that the hybrid structure of this social journalism platform allows a rapid detection of innovation phenomena, unlike other traditional sources. The technological convergence phenomenon has been modelled through a network-based approach, analysing the differences of networks computed during two time periods (pre and post COVID-19). The results led us to discuss the repurposing of technologies regarding "Remote Control", "Remote Working", "Health" and "Remote Learning".

cs.IR↗

arXiv2020

Lights and shadows of COVID-19, Technology and Industry 4.0

Scientific discoveries and technologies played a significant role in the digital revolution that occurred over the last years. But what is their role in the turmoil brought by the current pandemic? The aim of this paper is to show how digital technologies are operating during this first phase of the spreading of COVID-19. The study analyses and debates the current and potential role of digital technologies, focusing on their influence in the industrial and social fields. More specifically we used the blogging platform "Medium", which has seen an exponential growth in its production of articles over the last couple of months. Even if different from esteemed scientific sources, this platform provides a structure that suits our analysis. We searched how many times digital technologies are mentioned in articles regarding Coronavirus and, after collecting these articles, we collected page tags (comparable to "keywords" in scientific articles) and classified them (technology tags and non-technology tags), to create a graph showing the relation between them. This network allowed us to acknowledge and picture how technologies are currently debated. This was the starting point to discuss the key implications for an imminent future, and question about the impact on industry, society and labour market. What are the opportunities or threats of using technologies of Industry 4.0? Which are the needs rising because of the pandemic and how can technologies help in their fulfillment? How will the industrial scenario change after this pandemic? How will the labour market be affected? How can technologies be advantageous in the emerging social challenges?

cs.CY↗