2021/04/08 by Wilson Fearn, Orion Weller, Fearn, Wilson +3
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Text and Document Classification Technologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2104.03848
openalex publication_date 2021/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Text classification is a significant branch of natural language processing,\nand has many applications including document classification and sentiment\nanalysis. Unsurprisingly, those who do text classification are concerned with\nthe run-time of their algorithms, many of which depend on the size of the\ncorpus' vocabulary due to their bag-of-words representation. Although many\nstudies have examined the effect of preprocessing techniques on vocabulary size\nand accuracy, none have examined how these methods affect a model's run-time.\nTo fill this gap, we provide a comprehensive study that examines how\npreprocessing techniques affect the vocabulary size, model performance, and\nmodel run-time, evaluating ten techniques over four models and two datasets. We\nshow that some individual methods can reduce run-time with no loss of accuracy,\nwhile some combinations of methods can trade 2-5% of the accuracy for up to a\n65% reduction of run-time. Furthermore, some combinations of preprocessing\ntechniques can even provide a 15% reduction in run-time while simultaneously\nimproving model accuracy.\n