Abstract: A method, system, and computer program product for using a natural language processor is disclosed. Included are importing highlighted and non-highlighted training text each including training nodes, one-hot encoding the training text, training a projection model using the training text, processing the highlighted training text using the projection model, and training a classifier model using the highlighted processed training text. Also included are importing new text including new nodes, one-hot encoding the new text, processing the new text using the projection model, and determining, using the classifier model, whether one of the new nodes is in a sought-after class.

Abstract: A method, system, and computer program product for using a natural language processor includes importing a training text including a plurality of training nodes and having a first marked span that includes some of the plurality of training nodes and a first training trigger, wherein each training node includes a first attribute and a second attribute. Also included are generating a first plurality of training tree tables for each training node with respect to a first training traversal from the first training trigger, each training tree table including the first and second attributes of each training node in the first training traversal, and generating a first plurality of training comparison tables from the first plurality of training tree tables. Each training comparison table is also analyzed to determine if a target node belongs in the first marked span, and labeling each training comparison table accordingly.

Abstract: A method, system, and computer program product for using a natural language processor to find nodes in a span include providing a parse tree including a trigger node, a first target node connected to the trigger node by a first edge, and a second target node connected to the first target node by a second edge, wherein first trigger node includes a first attribute and a second attribute, and wherein the target node includes a third attribute and a fourth attribute. Further included are recording the first, second, third, and fourth attributes in a first tree table; creating a first consideration table from the first tree table, the first consideration table including the first, second, third, and fourth attributes; and evaluating the first target node to determine whether the first node belongs in a first span that includes the first trigger node.

Abstract: Methods, systems and computer readable media are provided for semantic evaluation of tentative triggers based on contextual triggers. Contextual triggers are identified within text. A parse tree comprising a plurality of nodes is generated corresponding to the text. Tentative triggers are identified within the text. A determination is made as to whether one or more nodes of the parse tree corresponding to the tentative trigger is within a context of one or more nodes of the parse tree corresponding to the contextual triggers. Based on the determination, the tentative trigger type is assigned to a contextual trigger type.

Abstract: Methods, systems and computer readable media are provided for semantic evaluation of tentative triggers based on contextual triggers. Contextual triggers are identified within text. A parse tree comprising a plurality of nodes is generated corresponding to the text. Tentative triggers are identified within the text. A determination is made as to whether one or more nodes of the parse tree corresponding to the tentative trigger is within a context of one or more nodes of the parse tree corresponding to the contextual triggers. Based on the determination, the tentative trigger type is assigned to a contextual trigger type.

Abstract: A method, system, and computer program product for using a natural language processor is disclosed. Included are importing highlighted and non-highlighted training text each including training nodes, one-hot encoding the training text, training a projection model using the training text, processing the highlighted training text using the projection model, and training a classifier model using the highlighted processed training text. Also included are importing new text including new nodes, one-hot encoding the new text, processing the new text using the projection model, and determining, using the classifier model, whether one of the new nodes is in a sought-after class.

Abstract: A method, system, and computer program product for using a natural language processor to find nodes in a span include providing a parse tree including a trigger node, a first target node connected to the trigger node by a first edge, and a second target node connected to the first target node by a second edge, wherein first trigger node includes a first attribute and a second attribute, and wherein the target node includes a third attribute and a fourth attribute. Further included are recording the first, second, third, and fourth attributes in a first tree table; creating a first consideration table from the first tree table, the first consideration table including the first, second, third, and fourth attributes; and evaluating the first target node to determine whether the first node belongs in a first span that includes the first trigger node.

Abstract: A method, system, and computer program product for using a natural language processor includes importing a training text including a plurality of training nodes and having a first marked span that includes some of the plurality of training nodes and a first training trigger, wherein each training node includes a first attribute and a second attribute. Also included are generating a first plurality of training tree tables for each training node with respect to a first training traversal from the first training trigger, each training tree table including the first and second attributes of each training node in the first training traversal, and generating a first plurality of training comparison tables from the first plurality of training tree tables. Each training comparison table is also analyzed to determine if a target node belongs in the first marked span, and labeling each training comparison table accordingly.