Multilingual Named Entity Recognition Solution for Optimizing Parcel Delivery in Online Commerce: Identifying Person and Organization Names

This paper presents a comprehensive solution to enhance parcel delivery in online commerce by implementing multilingual named entity recognition. The solution is designed to accurately identify person and organization names, with a primary emphasis on correctly identifying recipients. The ultimate goal is to use this information to automatically validate recipients and select the most accurate one to improve data accuracy and reliability for parcel delivery. The process begins by collecting a large dataset of online commerce data, including customer search queries, and annotating it with person and organization names. The data is then preprocessed, cleaned to eliminate irrelevant information, and prepared for training a named entity recognition model. Next, the model is trained and evaluated using this data to ensure its ability to identify named entities and extract recipients from queries accurately. The process employs an iterative training process and data generation techniques, while also addressing the issue of noisy data and iterative training introducing unwanted patterns by retraining the model on the subset of the original annotated dataset. Our experiments conclude a consistent increase of F1 score over the baseline and best iteration using this method of training and fine-tuning.