Application and evaluation of automated methods to extract neuroanatomical connectivity statements from free text

Motivation: Automated annotation of neuroanatomical connectivity statements from the neuroscience literature would enable accessible and large scale connectivity resources. Unfortunately, the connectivity findings are not formally encoded and occur as natural language text. This hinders aggregation, indexing, searching, and integration of the reports. We annotated a set of 1,377 abstracts for connectivity relations to facilitate automated extraction of connectivity relationships from neuroscience literature. We tested several baseline measures based on co-occurrence and lexical rules. We compare results from nine machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.