Active learning is a critical technique for reducing labelling load by selecting the most informative data. Most previous works applied active on Named Entity Recognition (token-level task) similar to text classification (sentence-level task). They failed consider heterogeneity of uncertainty within each sentence and required access entire annotator when labelling. To overcome mentioned limitat...

This presents a machine learning-based approach for disease named entity recognition and normalization (DNER) subtask of Chemical Disease Relation (CDR) task in BioCreative V. This approach employs a Conditional Random Fields (CRF) based model with domain specific features in biomedical area in disease named entity recognition. In order to improve the performance of entity normalization, the me...

Named entity recognition is a fundamental task in natural language processing. Based on the sequence labeling paradigm for flat named recognition, multiple methods have been developed to handle nested structures. However, they either require fixed order or introduce complex hypergraphs. To tackle this problem, we propose novel model Local Hypergraph Builder Network (LHBN) that builds simpler lo...

MOTIVATION Chemical named entity recognition is used to automatically identify mentions to chemical compounds in text and is the basis for more elaborate information extraction. However, only a small number of applications are freely available to identify such mentions. Particularly challenging and useful is the identification of International Union of Pure and Applied Chemistry (IUPAC) chemica...

This paper describes the system developed by the UTH-CCB team from the University of Texas Health Science Center at Houston (UTHealth), for the 2015 BioCreative V shared tasks of Track 3 on extraction of chemical disease relation (CDR). We participated in both tasks: Task A for “Disease Named Entity Recognition and Normalization (DNER)” and Task B for “Chemical-induced Diseases Relation Extract...