International Conference on Computational Linguistics · 2018 · 12 citations · 2 references
ComputingBiomedical InformaticsComputer ScienceInformation RetrievalKnowledge RepresentationNatural Language ProcessingBiomedical Concept NormalizationClinical Natural Language ProcessingData NormalizationHealth Information RetrievalInformation ExtractionQuestion AnsweringClinical Question AnsweringClinical Question Answering SystemsInteractive RetrievalInteractive Retrieval SystemsKnowledge Graph Question AnsweringKnowledge-based Question AnsweringQuestion UnderstandingTraditional Chinese Medicine
T-Know is a knowledge service system based on the constructed knowledge graph of Traditional Chinese Medicine (TCM). Using authorized and anonymized clinical records, medicine clinical guidelines, teaching materials, classic medical books, academic publications, etc., as data resources, the system extracts triples from free texts to build a TCM knowledge graph by our developed natural language processing methods. On the basis of the knowledge graph, a deep learning algorithm is implemented for single-round question understanding and multiple-round dialogue. In addition, the TCM knowledge graph also is used to support human-computer interactive knowledge retrieval by normalizing search keywords to medical terminology.
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Bidirectional LSTM-CRF Models for Sequence Tagging
Zhiheng Huang, Kai Yu · arXiv (Cornell University) · 2015 · 3.3K citations · Full text
Question Answering on Freebase via Relation Extraction and Textual Evidence
Kun Xu, Siva Reddy, Yansong Feng et al. · 2016 · 293 citations · Full text