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Pré-Publication, Document De Travail Année : 2023

Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain

Résumé

The rising influence of social media platforms in various domains, including tourism, has highlighted the growing need for efficient and automated natural language processing (NLP) approaches to take advantage of this valuable resource. However, the transformation of multilingual, unstructured, and informal texts into structured knowledge often poses significant challenges. In this work, we evaluate and compare few-shot, pattern-exploiting and fine-tuning machine learning techniques on large multilingual language models (LLMs) to establish the best strategy to address the lack of annotated data for 3 common NLP tasks in the tourism domain: (1) Sentiment Analysis, (2) Named Entity Recognition, and (3) Fine-grained Thematic Concept Extraction (linked to a semantic resource). Furthermore, we aim to ascertain the quantity of annotated examples required to achieve good performance in those 3 tasks, addressing a common challenge encountered by NLP researchers in the construction of domain-specific datasets. Extensive experimentation on a newly collected and annotated multilin
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Dates et versions

hal-04326748 , version 1 (06-12-2023)

Identifiants

Citer

Maxime Masson, Rodrigo Agerri, Christian Sallaberry, Marie-Noelle Bessagnet, Annig Le Parc Lacayrelle, et al.. Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain. 2023. ⟨hal-04326748⟩

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