A survey on text classification: Practical perspectives on the Italian language

Gasparetto, Andrea; Zangari, Alessandro; Marcuzzo, Matteo; Albarelli, Andrea

doi:10.1371/journal.pone.0270904

Cited by 5 publications

(2 citation statements)

References 107 publications

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“…Text classification is one of the most active research areas within NLP, and many surveys and reviews have been published in recent years. These cover a wide range of techniques used in NLP, from traditional methods to the latest deep learning applications [1,2,[11][12][13]. However, these works cover the broader TC field and do not cover HTC specifically (or mention it very briefly).…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Text Classification and Its Foundations: A Review of Current Research

Zangari,

Marcuzzo,

Rizzo

et al. 2024

Electronics

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While collections of documents are often annotated with hierarchically structured concepts, the benefits of these structures are rarely taken into account by classification techniques. Within this context, hierarchical text classification methods are devised to take advantage of the labels’ organization to boost classification performance. In this work, we aim to deliver an updated overview of the current research in this domain. We begin by defining the task and framing it within the broader text classification area, examining important shared concepts such as text representation. Then, we dive into details regarding the specific task, providing a high-level description of its traditional approaches. We then summarize recently proposed methods, highlighting their main contributions. We also provide statistics for the most commonly used datasets and describe the benefits of using evaluation metrics tailored to hierarchical settings. Finally, a selection of recent proposals is benchmarked against non-hierarchical baselines on five public domain-specific datasets. These datasets, along with our code, are made available for future research.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Text Classification and Its Foundations: A Review of Current Research

Zangari,

Marcuzzo,

Rizzo

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

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“…Text classification is a common task in natural language processing (NLP), and most studies focus on English corpora [1]. However, some hieroglyphic languages, such as Chinese, have unique characteristics that differ from alphabetic languages.…”

Section: Introductionmentioning

confidence: 99%

Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

Hou

Wang

2023

PLoS ONE

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As hieroglyphic languages, such as Chinese, differ from alphabetic languages, researchers have always been interested in using internal glyph features to enhance semantic representation. However, the models used in such studies are becoming increasingly computationally expensive, even for simple tasks like text classification. In this paper, we aim to balance model performance and computation cost in glyph-aware Chinese text classification tasks. To address this issue, we propose a lightweight ensemble learning method for glyph-aware Chinese text classification (LEGACT) that consists of typical shallow networks as base learners and machine learning classifiers as meta-learners. Through model design and a series of experiments, we demonstrate that an ensemble approach integrating shallow neural networks can achieve comparable results even when compared to large-scale transformer models. The contribution of this paper includes a lightweight yet powerful solution for glyph-aware Chinese text classification and empirical evidence of the significance of glyph features for hieroglyphic language representation. Moreover, this paper emphasizes the importance of assembling shallow neural networks with proper ensemble strategies to reduce computational workload in predictive tasks.

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Recommendation Systems: An Insight Into Current Development and Future Research Challenges

et al. 2022

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Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets.

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A survey on text classification: Practical perspectives on the Italian language

Cited by 5 publications

References 107 publications

Hierarchical Text Classification and Its Foundations: A Review of Current Research

Hierarchical Text Classification and Its Foundations: A Review of Current Research

Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

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