It is important to understand the chemicals contained in plants because these chemicals can regulate activities of proteins that are key factors in causing diseases. With the accumulation of a large volume of biomedical literature in various databases such as PubMed, it is possible to automatically extract relationships between plants and chemicals in a large-scale way if we apply a text mining approach. Thus, we constructed a corpus for plant and chemical entities and for the relationships between them to develop and evaluate such text mining approaches. The corpus currently contains 267 plant entities, 475 chemical entities, and 1,007 plant-chemical relationships (550 and 457 positive and negative relationships, respectively), which are drawn from 377 sentences in 245 PubMed abstracts. Inter-annotator agreement scores for the corpus among three annotators were measured. The simple percent agreement scores for entities and triggers words for the relationships were 99.6% and 94.8%, respectively, and the overall kappa score for the classification of positive and negative relationships was 79.8%. We also developed a rule-based model to automatically extract such plant-chemical relationships. When we evaluated the rule-based model using the corpus and randomly selected biomedical articles, overall F-scores of 68.0% and 61.8% were achieved, respectively.
- Corpus Example
The corpus follows the BioC format.