Abstract: A machine reading system is described herein that includes a framework in which grammar rules can be developed using a concise language that combines syntax and semantics. The resulting technology thus reduces the development time for new grammars in a new domain. An enormous amount of information appears in the form of natural language across millions of academic papers and other literature sources. For example, in the biological domain, there is a tremendous ongoing effort to extract individual chemical interactions from these texts, but these interactions are only isolated fragments of larger causal mechanisms such as protein signaling pathways. The proposed rule-based event extraction framework can model underlying syntactic representations of events in order to extract signaling pathway fragments. Though application to the biomedical domain is herein described, the framework is domain-independent and is expressive enough to capture most complex events annotated by domain experts.

Abstract: A domain fact verification system is described having a computer programmed with a model trained using a process of data distillation and model distillation to improve model learning of the underlying semantics of a dataset rather than relying on statistical and lexical nuances in a domain-specific dataset. The computer thus programmed can accurately perform fact verification across multiple domains without the labor-intensive process of encoding a dataset of human-annotated, domain-specific information for each domain. Moreover, by combining data distillation with model distillation techniques, which may be seen as an inverse of well-established ensemble strategies (which train individual models separately and applies them jointly) the present domain transferable fact verification system scales better at inference time due to its reliance on a single trained model.

Abstract: A machine reading system is described herein that includes a framework in which grammar rules can be developed using a concise language that combines syntax and semantics. The resulting technology thus reduces the development time for new grammars in a new domain. An enormous amount of information appears in the form of natural language across millions of academic papers and other literature sources. For example, in the biological domain, there is a tremendous ongoing effort to extract individual chemical interactions from these texts, but these interactions are only isolated fragments of larger causal mechanisms such as protein signaling pathways. The proposed rule-based event extraction framework can model underlying syntactic representations of events in order to extract signaling pathway fragments. Though application to the biomedical domain is herein described, the framework is domain-independent and is expressive enough to capture most complex events annotated by domain experts.