Abstract
•An improved deep learning model using CRF and Bi-LSTM is given to achieve named entity recognition with unbalanced labels.•An adaptive G-Score is used to compare the fitting ability of models by evaluating the gap between Precision and Recall.•A multiclassification cross-entropy loss function has been presented for training with additional weights.
With the development of cutting-edge IT technologies, e.g. Big Data, Knowledge Engineering, etc., traditional Intellectual Property (IP) services have depicted high redundancy and low efficiency during management of such large-scale of data. Recent advancement of Artificial Intelligence (AI) and Deep Learning (DL) models has been accelerating relevant research activities being investigated on Knowledge Graph (KG) schemes and applications in different domains, such as medical services, social media, etc. However, when IP services and their cyber-social provision are taken into account, relevant approaches suffer from unbalanced labels against training results, and inappropriate evaluation metrics not well reflecting the impact of the unbalance. In this paper, a deep learning model combining Conditional Random Field and Bidirectional LSTM has been proposed, in order to achieve named entity recognition with unbalanced labels. An adaptive metric, G-Score was introduced to compare the fitting ability of models by evaluating the gap between Precision and Recall. According to the results, the proposed model can effectively recognize the potential named entities with outperformance over other relevant models.