The Text Classification Pipeline: Starting Shallow going Deeper

• URL: http://arxiv.org/abs/2501.00174v1
• Date: Mon, 30 Dec 2024 23:01:19 GMT
• Title: The Text Classification Pipeline: Starting Shallow going Deeper
• Authors: Marco Siino, Ilenia Tinnirello, Marco La Cascia,
• Abstract summary: Text Classification (TC) stands as a cornerstone within the realm of Natural Language Processing (NLP) This monograph provides an in-depth exploration of the TC pipeline, with a particular emphasis on evaluating the impact of each component on the overall performance of TC models. The pipeline includes state-of-the-art datasets, text preprocessing techniques, text representation methods, classification models, evaluation metrics, current results and future trends.
• Score: 4.97309503788908
• License:
• Abstract: Text Classification (TC) stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through the lens of computer science and engineering. The past decade has seen deep learning revolutionize TC, propelling advancements in text retrieval, categorization, information extraction, and summarization. The scholarly literature is rich with datasets, models, and evaluation criteria, with English being the predominant language of focus, despite studies involving Arabic, Chinese, Hindi, and others. The efficacy of TC models relies heavily on their ability to capture intricate textual relationships and nonlinear correlations, necessitating a comprehensive examination of the entire TC pipeline. This monograph provides an in-depth exploration of the TC pipeline, with a particular emphasis on evaluating the impact of each component on the overall performance of TC models. The pipeline includes state-of-the-art datasets, text preprocessing techniques, text representation methods, classification models, evaluation metrics, current results and future trends. Each chapter meticulously examines these stages, presenting technical innovations and significant recent findings. The work critically assesses various classification strategies, offering comparative analyses, examples, case studies, and experimental evaluations. These contributions extend beyond a typical survey, providing a detailed and insightful exploration of TC.

Related papers

• Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks [12.087640144194246]
  This paper conducts a comparative study on three deep learning models:TextCNN, TextRNN, and FastText.specifically for Chinese text classification tasks. The performance of these models is evaluated, and their applicability in different scenarios is discussed.
  arXiv  Detail & Related papers  (2024-12-25T13:54:40Z)
• Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese [4.772998830872483]
  We develop a pipeline for historical-psychological text analysis in classical Chinese. The pipeline combines expert knowledge in psychometrics with text representations generated via transformer-based language models. Considering the scarcity of available data, we propose an indirect supervised contrastive learning approach.
  arXiv  Detail & Related papers  (2024-03-01T13:14:45Z)
• A Systematic Review of Data-to-Text NLG [2.4769539696439677]
  Methods for producing high-quality text are explored, addressing the challenge of hallucinations in data-to-text generation. Despite advancements in text quality, the review emphasizes the importance of research in low-resourced languages.
  arXiv  Detail & Related papers  (2024-02-13T14:51:45Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Multi-Dimensional Evaluation of Text Summarization with In-Context Learning [79.02280189976562]
  In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization. We then analyze the effects of factors such as the selection and number of in-context examples on performance.
  arXiv  Detail & Related papers  (2023-06-01T23:27:49Z)
• OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models [122.27878464009181]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)
• Deep Learning Based Text Classification: A Comprehensive Review [75.8403533775179]
  We provide a review of more than 150 deep learning based models for text classification developed in recent years. We also provide a summary of more than 40 popular datasets widely used for text classification.
  arXiv  Detail & Related papers  (2020-04-06T02:00:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.