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.

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 clinical event management and communications system for use in augmenting the electronic health records (EHRs) of a healthcare facility, which uses interfaces to access the same, for enhancing nurse and other healthcare provider decision-making and communications of patient event and other information.

Abstract: Presented herein are systems and method for generating and/or using a classifier that can identify or classify entities, such as (by way of illustration and not limitation) whether an entity in a contested proceeding is a patent monetizing entity (PME). In embodiments, using features extracted from various sources such as, by way of example and not limitation, the entities' litigation behavior, the patents they asserted, and their presence on the web, a classifier can correctly separates PMEs from operating companies with a reasonable degree of accuracy. In embodiments, one or more classifier may be trained to classify or label entities into one of a plurality of classes. Such classifiers can be useful tools for policy makers and others, allowing them to gain a clearer picture of contested proceedings filed to date and assessing newly filed cases in real time.