Leveraging Gene Ontology Annotations to Improve a Memory-Based Language Understanding System

Abstract

This work evaluates how detailed knowledge about proteins can be leveraged for language understanding and disambiguation by OpenDMAP. OpenDMAP is a memory-based language understanding system that uses patterns to identify concepts in text. These patterns match not only lexical elements, such as words, but also semantic elements, such as references to proteins. This work started with an existing pattern set used to extract biological activation events from a corpus of GeneRIFs (sentences or phrases that each describe one of many of the functions of a gene). This is a challenging task because many distinct activation concepts, in addition to being semantically similar, are described using very..