Abstract Named Entity Recognition (NER), one of the most fundamental problems in natural
language processing, seeks to identify the boundaries and types of entities with specific …

Chinese named entity recognition is a crucial initial step of information extraction in the field
of agricultural diseases and pests. This step aims to identify named entities related to …

Named Entity Recognition (NER) is a natural language processing task for recognizing
named entities in a given sentence. Chinese NER is difficult due to the lack of delimited …

Named entity recognition (NER) of chemicals and drugs is a critical domain of information
extraction in biochemical research. NER provides support for text mining in biochemical …

In recent years, deep learning has greatly improved the performance of named entity
recognition models in various fields, especially in the agricultural domain. However, most …

Clinical named entity recognition (CNER) identifies entities from unstructured medical
records and classifies them into predefined categories. It is of great significance for follow …

High quality agricultural named entity recognition (NER) model can provide effective support
for agricultural information extraction, semantic retrieval and other tasks. However, the …

This paper describes the ROCLING-2022 shared task for Chinese healthcare named entity
recognition, including task description, data preparation, performance metrics, and …

For the problem of long modeling time and large workload of multiview bill of material
(XBOM) reconstruction process in digital twin shop, this paper proposes a knowledge-driven …

Named Entity Recognition (NER) is the fundamental task for Natural Language Processing
(NLP) and the initial step in building a Knowledge Graph (KG). Recently, BERT …