Extracting and Visualizing Semantic Relationships from Chinese Biomedical Text

In this paper, we study how to automatically extract and visualize food (or nutrition) and disease relationships from Chinese publications of Nutritional Genomics. Different from previous approaches that mostly apply handcrafted rules or co-occurrence patterns, we propose an approach using probabilistic models and domain knowledge. In particular, we first utilize encyclopedia to construct a domain knowledge base, and then develop a sentence simplification model to simplify complicated sentences we meet. Afterwards, we treat relation extraction issue as a sequence labeling task and adopt Conditional Random Fields (CRFs) models to extract food and disease relationships. Finally, these relationships are visualized. Experimental results on real-world datasets show that the proposed approach is effective.