Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species - Gabud, Roselyn; Lapitan, Portia; Mariano, Vladimir; Mendoza, Eduardo; Pampolina, Nelson; Clariño, Maria Art Antonette; Batista-Navarro, Riza (Frontiers in Artificial Intelligence, 2024)
Fine-grained, descriptive information on habitats and reproductive conditions of plant species are crucial in forest restoration and rehabilitation efforts. Precise timing of fruit collection and knowledge of species' habitat preferences and reproductive status are necessary especially for tropical plant species that have short-lived recalcitrant seeds, and those that exhibit complex reproductive patterns, e.g., species with supra-annual mass flowering events that may occur in irregular intervals. Understanding plant regeneration in the way of planning for effective reforestation can be aided by providing access to structured information, e.g., in knowledge bases, that spans years if not decades as well as covering a wide range of geographic locations. on an annotated corpus of biodiversity-focused documents demonstrated an improvement of up to 15 percentage points in recall and best performance over solely rule-based and transformerbased methods with F1-scores ranging from 89.61% to 96.75% for reproductive conditiontemporal expression relations, and ranging from 85.39% to 89.90% for habitat -geographic location relations. Our work shows that even without training models on any domain-specific labeled dataset, we are able to extract relationships between biodiversity concepts from literature with satisfactory performance.