Extended Pre-Processing Pipeline For Text Classification: On the Role of Meta-Features, Sparsification and Selective Sampling

Washington Cunha, Leonardo Rocha, Marcos André Gonçalves
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Abstract

Pipelines for Text Classification are sequences of tasks needed to be performed to classify documents. The pre-processing phase of these pipelines involves different ways of manipulating documents for the learning phase. This Master Thesis introduces three new steps into the traditional pre-processing phase: 1) Meta-Features Generation; 2) Sparsification; and 3) Selective Sampling. Our experimental results, based on more than 5.600 measurements, show that our proposal can achieve significant gains in effectiveness when compared to the traditional TF-IDF representation (up to 52%) and word embeddings (up to 46%), at a much lower cost (9.7x faster). Our Master Thesis also includes a thorough and rigorous evaluation of the trade-offs between cost and effectiveness associated with the introduction of these new steps into the pipeline, as well as a comprehensive comparative experimental evaluation of many alternatives. This thesis falls under the topics of (i) Document Management and Classification, (ii) Information Retrieval Models and Techniques, (iii) and Text Database of the SBBD Call for Papers.
文本分类的扩展预处理管道:论元特征、稀疏化和选择性抽样的作用
文本分类管道是对文档进行分类所需执行的一系列任务。这些管道的预处理阶段涉及为学习阶段操作文档的不同方法。本硕士论文在传统的预处理阶段引入了三个新的步骤:1)元特征生成;2) Sparsification;3)选择性抽样。基于超过5600个测量的实验结果表明,与传统的TF-IDF表示(高达52%)和词嵌入(高达46%)相比,我们的提议可以实现显著的有效性提升,成本低得多(速度快9.7倍)。我们的硕士论文还包括对引入这些新步骤的成本和效率之间的权衡进行彻底和严格的评估,以及对许多替代方案进行全面的比较实验评估。本文的主题为:(1)文献管理与分类,(2)信息检索模型与技术,(3)SBBD征文文本数据库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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