@inproceedings{alva-principe-etal-2024-lcf,
    title = "An {LCF}-{IDF} Document Representation Model Applied to Long Document Classification",
    author = "Alva Principe, Renzo Arturo  and
      Chiarini, Nicola  and
      Viviani, Marco",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.101",
    pages = "1129--1135",
    abstract = "A document representation model that has been used for years in NLP and Text Mining tasks is TF-IDF (Term Frequency-Inverse Document Frequency). This model is indeed effective for various tasks like Information Retrieval and Document Classification. However, it may fall short when it comes to capturing the deeper semantic and contextual meaning of a text, which is where Transformer-based Pre-trained Language Models (PLMs) such as BERT have been gaining significant traction in recent years. Despite this, these models also face specific challenges related to Transformers and their attention mechanism limits, especially when dealing with long documents. Therefore, this paper proposes a novel approach to exploit the advantages of the TF-IDF representation while incorporating semantic context, by introducing a Latent Concept Frequency-Inverse Document Frequency (LCF-IDF) document representation model. Its effectiveness is tested with respect to the Long Document Classification task. The results obtained show promising performance of the proposed solution compared to TF-IDF and BERT-like representation models, including those specifically for long documents such as Longformer as well as those designed for particular domains, especially when it comes to Single Label Multi-Class (SLMC) classification.",
}

A document representation model that has been used for years in NLP and Text Mining tasks is TF-IDF (Term Frequency-Inverse Document Frequency). This model is indeed effective for various tasks like Information Retrieval and Document Classification. However, it may fall short when it comes to capturing the deeper semantic and contextual meaning of a text, which is where Transformer-based Pre-trained Language Models (PLMs) such as BERT have been gaining significant traction in recent years. Despite this, these models also face specific challenges related to Transformers and their attention mechanism limits, especially when dealing with long documents. Therefore, this paper proposes a novel approach to exploit the advantages of the TF-IDF representation while incorporating semantic context, by introducing a Latent Concept Frequency-Inverse Document Frequency (LCF-IDF) document representation model. Its effectiveness is tested with respect to the Long Document Classification task. The results obtained show promising performance of the proposed solution compared to TF-IDF and BERT-like representation models, including those specifically for long documents such as Longformer as well as those designed for particular domains, especially when it comes to Single Label Multi-Class (SLMC) classification.
Alva Principe, Renzo Arturo Chiarini, Nicola Viviani, Marco
An LCF-IDF Document Representation Model Applied to Long Document Classification