2023 LinearClassifierAnOftenForgotte

From GM-RKB
Jump to navigation Jump to search

Subject Headings: Supervised Text Classification Algorithm.

Notes

Cited By

Quotes

Abstract

Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often directly train them for a few epochs and deploy the obtained model. In this opinion paper, we point out that this way may not always get satisfactory results. We argue the importance of running a simple baseline like linear classifiers on bag-of-words features along with advanced methods. First, for many text data, linear methods show competitive performance, high efficiency, and robustness. Second, advanced models such as BERT may only achieve the best results if properly applied. Simple baselines help to confirm whether the results of advanced models are acceptable. Our experimental results fully support these points.

BODY

...

ngram_range: Specify the range of n-grams to be extracted. For example, LibMultiLabel only uses uni-gram while Chalkidis et al. (2022) set ngram_range to (1, 3) so uni-gram, bi-gram, and tri-gram are extracted into the vocabulary list for a richer representation of the document. min_df: The parameter is used for removing infrequent tokens. Chalkidis et al. (2022) remove tokens that appear in less than five documents while LibMultiLabel does not remove any tokens. max_features: The parameter decides the number of features to use by term frequency. For example, Chalkidis et al. (2022) consider the top 10,000, 20,000, and 40,000 frequent terms as the search space of the parameter ...

...

Table 6: Data statistics for LexGLUE, the benchmark considered in Chalkidis et al. (2022). W means the average # words per instance of the whole set. The # features indicates the # TF-IDF features used by linear methods ...

...

References

;

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2023 LinearClassifierAnOftenForgotteChih-Jen Lin
Yu-Chen Lin
Si-An Chen
Jie-Jyun Liu
Linear Classifier: An Often-Forgotten Baseline for Text Classification10.48550/arXiv.2306.071112023