A Research Toward Chinese Named Entity Recognition Based on Transfer Learning

Abstract

To improve the performance of named entity recognition in the lack of well-annotated entity data, a transfer learning-based Chinese named entity recognition model is proposed in this paper. The specific tasks are as follows: (1) first/, a data transfer method based on entity features is proposed. By calculating the similarity of feature distribution between low resource data and high resource data, the most representative entity features are selected for feature transfer mapping, and the distance of entity distribution between the two domains is calculated to make up the gap between the data of the two domains then model is trained by high resource data. (2) Then, an entity boundary detection method is proposed. This method utilizes the BiLSTM+CRF as the main structure and integrates character boundary information to assist the attention network to improve the model’s ability to recognize entity boundaries. (3) Finally, multiple named entity recognition methods are selected as baseline methods for comparison, and experiments are conducted on several datasets. The results show that the model proposed in this paper improves the accuracy of named entity recognition by 1%, the recall rate by 2%, and the F1 value by 2% on average in the field with low-resource.

Analysis of Multi-Intelligent Distributed Japanese Language Block Recognition Based on Knowledge Recognition Corpus

“…Multi-Intelligent Distributed Systems (MIDS) integrated with the Knowledge Recognition Corpus (KRC) present a formidable combination in the realm of artificial intelligence and natural language processing [12]. By incorporating KRC's extensive dataset, MIDS gain access to a rich source of factual information, entities, concepts, and relationships, enabling distributed agents to make informed decisions and perform complex tasks with greater accuracy and efficiency [13].…”