Abstract
In practical applications within the e-commerce domain, there is often a requirement to identify product entities and their corresponding brand entities, based on their descriptions. However, there has been a relatively limited focus on studies addressing the Named Entities Recognition in the e-commerce domain. we crawled data from e-commerce websites and transformed them into a Named Entity Recognition dataset, which is suitable for Machine Reading Comprehension. Since the questions Machine Reading Comprehension contain a priori semantic information about the types of the entities, we propose a model that uses the MRC modeling paradigm to solve the task of recognizing brand entities as well as commodity entities in the e-commerce domain. The model encodes the contexts and the corresponding questions using the RoBERTa-wwm model, and then further extracts the semantic information of the contexts using an attention network. We utilize SoftMax as the decoding layer to get the head index and tail index of the entity, and finally use the matching module to get the entity index. Through experiments on two e-commerce datasets, the results show that the new method can significantly improve the recognition effect of Chinese NER in e-commerce domain.
Publisher
Darcy & Roy Press Co. Ltd.