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arXiv · 2302.06155

Identifying Semantically Difficult Samples to Improve Text Classification

Shashank Mujumdar·Stuti Mehta·Hima Patel·Suman Mitra
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Abstract

In this paper, we investigate the effect of addressing difficult samples from a given text dataset on the downstream text classification task. We define difficult samples as being non-obvious cases for text classification by analysing them in the semantic embedding space; specifically - (i) semantically similar samples that belong to different classes and (ii) semantically dissimilar samples that belong to the same class. We propose a penalty function to measure the overall difficulty score of every sample in the dataset. We conduct exhaustive experiments on 13 standard datasets to show a consistent improvement of up to 9% and discuss qualitative results to show effectiveness of our approach in identifying difficult samples for a text classification model.

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Shashank Mujumdar, Stuti Mehta, Hima Patel, Suman Mitra. 2023-02-13. Identifying Semantically Difficult Samples to Improve Text Classification. https://arxiv.org/abs/2302.06155

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