wbsg-uni-mannheim / productcategorization

This repository contains code and data download instructions for the workshop paper "Improving Hierarchical Product Classification using Domain-specific Language Modelling" by Alexander Brinkmann and Christian Bizer.

Improving Hierarchical Product Classification using Domain-specific Language Modelling

In order to deliver a coherent user experience, product aggregators like market places or price portals integrate product offers from many web shops into a single product hierarchy. Recently, transformer models have shown remarkable performance on various NLP tasks. These models are pre-trained on huge cross-domain text corpora using self-supervised learning and fine-tuned afterwards for specific downstream tasks. Research from other application domains indicates that additional self-supervised pre-training using domain-specific text corpora can further increase downstream performance without requiring additional task-specific training data.

In this paper we first show that transformers outperform a more traditional fastText-based classification technique on the task of assigning product offers from different web shops into a single product hierarchy. Afterwards, we investigate whether it is possible to further improve the performance of the transformer model by performing additional self-supervised pre-training using different corpora of product offers which were extracted from the Common Crawl. Our experiments show that by using large numbers of related product offers together with the heterogeneous categorization information from the original web shops for masked language modelling, it is possible to further increase the performance of the transformer model by 1.22% in wF1 and 1.36% in hF1 reaching a performance of nearly 89% wF1. All source code to reproduce our results is available in this repository.

The data needed to evaluate the results is online available as well:

1. MWPD Data set
2. Icecat Data set
3. Language Modelling - Product Corpora