Named Entity Recognition for Brazilian Portuguese Product Titles

Abstract

Improving the interaction between consumers and marketplaces, focusing on reaching higher conversion rates is one of the main goals of e-commerce companies. Offering better results for user queries is mandatory to improve user experience and convert it into purchases. This paper investigates how named entity recognition can extract relevant attributes from product titles to derive better filters for user queries. We conducted several experiments based on MITIE and BERT applied to smartphones/cellphones product titles from the largest Brazilian retail e-commerce. Both of our strategies achieve outstanding results with a general F1 score of around 95%. We concluded that using a classical machine learning pipeline is still more useful than relying on large pre-trained language models, considering the model’s throughput and efficiency. Future work may focus on evaluating the scalability and reusability capacity of both approaches.