Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework difficult to detect entities with nested structures. In this work, we view NER as constituency parsing partially-observed trees and model it TreeCRFs. Specifically, all labeled spans observed nodes tree, other latent nodes. With TreeCRF achieve uniform way j...

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It found the literature that several researchers have extensively machine learning models for NER.The fundamental tasks among medical data mining are and normalization. Medical different general NER in various ways. Huge number of alternate spellings synonyms create e...

Nowadays, most deep learning models ignore Chinese habits and global information when processing tasks. To solve this problem, we constructed the BERT-BiLSTM-Attention-CRF model. In model, embeded BERT pre-training language model that adopts Whole Word Mask strategy, added a document-level attention. Experimental results show our method achieves good in MSRA corpus, F1 reaches 95.00%.

Biomedical named entity recognition is Key technology for automatic processing of health medical information. However, it labor-expensive get enough labeled data and always facing low-resource situation. We propose a method biomedical that under Our work based on the 8th China Health Information Processing Conference task-1 ranked third among all teams. In final results test set, precision $$38...