A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.
Rosario and Hearst employ neural networks using lexical, syntactic, and semantic features for distinguishing seven relations between entities "disease" and "treatment" REF .
Classifying Semantic Relations In Bioscience Texts
Annual Meeting Of The Association For Computational Linguistics
Computer Science