  Data augmentation techniques are widely used in text classification tasks to
improve the performance of classifiers, especially in low-resource scenarios.
Most previous methods conduct text augmentation without considering the
different functionalities of the words in the text, which may generate
unsatisfactory samples. Different words may play different roles in text
classification, which inspires us to strategically select the proper roles for
text augmentation. In this work, we first identify the relationships between
the words in a text and the text category from the perspectives of statistical
correlation and semantic similarity and then utilize them to divide the words
into four roles -- Gold, Venture, Bonus, and Trivial words, which have
different functionalities for text classification. Based on these word roles,
we present a new augmentation technique called STA (Selective Text
Augmentation) where different text-editing operations are selectively applied
to words with specific roles. STA can generate diverse and relatively clean
samples, while preserving the original core semantics, and is also quite simple
to implement. Extensive experiments on 5 benchmark low-resource text
classification datasets illustrate that augmented samples produced by STA
successfully boost the performance of classification models which significantly
outperforms previous non-selective methods, including two large language
model-based techniques. Cross-dataset experiments further indicate that STA can
help the classifiers generalize better to other datasets than previous methods.
