In the era of big data, E-commerce plays an increasingly important role, and steel E-commerce certainly occupies a positive position. However, it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs. In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms, we propose a novel deep learning-based loss function for named entity recognition (NER). Considering the impacts of small sample and imbalanced data, in our NER scheme, the focal loss, the label smoothing, and the cross entropy are incorporated into a lite bidirectional encoder representations from transformers (BERT) model to avoid the over-fitting. Moreover, through the analysis of different classic annotation techniques used to tag data, an ideal one is chosen for the training model in our proposed scheme. Experiments are conducted on Chinese steel E-commerce datasets. The experimental results show that the training time of a lite BERT (ALBERT)-based method is much shorter than that of BERT-based models, while achieving the similar computational performance in terms of metrics precision, recall, and F₁ with BERT-based models. Meanwhile, our proposed approach performs much better than that of combining Word2Vec, bidirectional long short-term memory (Bi-LSTM), and conditional random field (CRF) models, in consideration of training time and F₁.

Named entity recognition; bidirectional encoder representations from transformers; steel E-commerce platform; annotation technique