Name
Abstract | ||
|---|---|---|
Traditional Chinese medicine (TCM) has accumulated amount of literature with a total of 1,059 volumes, more than 190,000 chapters, and more than 120,000,000 words during the last 2000 years. In the previous works, researchers annotated the phrases one by one with their own hands. Here we propose semantic annotation techniques based on Semantic units division and annotation are realized through constructing a corpus and professional semantic unit dictionary. Based on the technology, a semantic annotation method is implemented using hidden markov models, which achieves 92.2% in terms of micro-average F1 measure and 87.6% in terms of macro-average F1 measure on the case of spleen putty genre. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BIBM.2014.6999320 | BIBM |
Keywords | Field | DocType |
semantic annotation method,professional semantic unit dictionary,traditional chinese medicine,semantic networks,spleen putty genre,ancient medical literature semantic annotation,dictionaries,ancient literature,semantic annotation techniques,semantic annotation,tcm,micro-average f1 measure,medical computing,hidden markov models,semantic unit division,patient treatment,macro-average f1 measure,physiology,semantics,databases | Semantic technology,Computer science,Artificial intelligence,Natural language processing,Medical literature,Semantic computing,Semantic compression,Semantic similarity,Annotation,Bioinformatics,Hidden Markov model,Machine learning,Semantics | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
4 | 7 | |