# Text Classification for Predicting Multi-level Product Categories

arXiv: https://arxiv.org/abs/2109.01084 · HTML: https://mlsanity.com/paper/2109.01084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders](https://mlsanity.com/paper/2012.15547/) (2020)
- [An Analysis of Hierarchical Text Classification Using Word Embeddings](https://mlsanity.com/paper/1809.01771/) (2018)
- [SIGIR 2021 E-Commerce Workshop Data Challenge](https://mlsanity.com/paper/2104.09423/) (2021)
- [Auto Response Generation in Online Medical Chat Services](https://mlsanity.com/paper/2104.12755/) (2021)
- [Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding](https://mlsanity.com/paper/1812.01885/) (2018)

## Built on by (most cited first, 1 of 1)

- [Multi-level Product Category Prediction through Text Classification](https://mlsanity.com/paper/2403.01638/) (2024)

## Abstract (arXiv)

In an online shopping platform, a detailed classification of the products facilitates user navigation. It also helps online retailers keep track of the price fluctuations in a certain industry or special discounts on a specific product category. Moreover, an automated classification system may help to pinpoint incorrect or subjective categories suggested by an operator. In this study, we focus on product title classification of the grocery products. We perform a comprehensive comparison of six different text classification models to establish a strong baseline for this task, which involves testing both traditional and recent machine learning methods. In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages. Our numerical results indicate that dynamic masking of subcategories is effective in improving prediction accuracy. In addition, we observe that using bilingual product titles is generally beneficial, and neural network-based models perform significantly better than SVM and XGBoost models. Lastly, we investigate the reasons for the misclassified products and propose future research directions to further enhance the prediction models.