Semantic taxonomy enrichment to improve business text classification for dynamic environments
Muhammad Arslan, Christophe Cruz · 2022
Taxonomies are widely used by various business organizations for document classification and organization. Business models built using taxonomies have the potential to reduce their efficiency with the arrival of new business ideas and concepts in the market over time. This happens because outdated business taxonomies get insufficient to fully capture the intended meaning of business documents to perform classification. Experts may need time to understand and engineering to place new data into the taxonomy to increase the classification accuracy of business documents. Here, the idea of automatic semantic enrichment of taxonomies came into consideration. This study introduces a taxonomy enrichment approach based on Natural Language Processing (NLP) technique, i.e. BERTopic, a Neural topic modeling using contextualized Bidirectional Encoder Representations from Transformers (BERT) to automatically augment a given business taxonomy with many additional concepts by leveraging a corpus of online news documents. The experiments show that augmenting topics from the text corpus into the taxonomy substantially increases the classification accuracy of business documents.