Improving Distantly-Supervised Named Entity Recognition for Traditional Chinese Medicine Text via a Novel Back-Labeling Approach.
Dezheng ZhangChao XiaCong XuQi JiaShibing YangXiong LuoYonghong XiePublished in: IEEE Access (2020)
Keyphrases
- named entity recognition
- text summarization
- traditional chinese medicine
- semi supervised
- named entities
- text mining
- information extraction
- natural language processing
- named entity disambiguation
- proper names
- sequence labeling
- unsupervised learning
- named entity recognizer
- maximum entropy
- active learning
- relation extraction
- pos taggers
- conditional random fields
- information retrieval
- weakly supervised
- annotated corpus
- supervised learning
- data mining
- labeled data
- pairwise
- natural language
- machine learning
- question answering
- generative model
- knowledge representation
- domain knowledge
- image segmentation
- feature selection
- learning algorithm
- real world