Abstract: A method includes: pre-processing initial data of an acquired to-be-analyzed confrontation scenario, to obtain triplet data marked in a graph data structure, inputting the triplet data into a pre-trained analyzing model, to determine a result of analysis on semantic information of the to-be-analyzed confrontation scenario; wherein the analyzing model includes a plurality of operator networks that are provided with execution priorities, wherein the plurality of operator networks are configured for analyzing relations of different types; and the processing result of any one of the operator networks includes: in the relation type corresponding to the operator network, respective true relations of all of the node pairs, and confidences and descriptive values of the true relations; and according to the graph data structure that is updated by using the processing results of all of the operator networks, characterizing the result of analysis on the semantic information of the to-be-analyzed confrontation scenario.

Abstract: A computer-implemented method for clinical trial results endpoint-based analysis and dynamic aggregation, comprising the steps of receiving one or more selected clinical trials, wherein the one or more selected clinical trials match a specification; obtaining clinical trial results for the one or more selected clinical trials from at least one external data source; and interpreting, via a machine learning model, the obtained clinical trial results; importing the obtained clinical trial results as structured data. The method further comprising the steps of matching, based on a similarity analysis, via a processor, clinical trial endpoints identified in the obtained clinical trial results to corresponding normalized endpoint options; aggregating, based on the matched corresponding normalized endpoint options, the obtained clinical trial results to determine aggregated results; and providing the aggregated results.

Abstract: A computer-implemented method, comprising the steps of analyzing a group of content items to determine a corresponding vector representation for each content item in the group of content items, the corresponding vector representation comprising a set of identifiers representing the corresponding content item in the group of content items; receiving, a search specification; identifying a relevant initial subset of content items in the group of content items; calculating a centroid vector; analyzing the corresponding vector representations of at least a portion of content items in the group of content items to determine relative relevancies of the at least the portion of content items in the group of content items; and providing an interactive graphical visualization of at least a segment of the relevant initial subset of content items in the group of content items and one or more other content items in the group of content items.

Abstract: A computer-implemented method, comprising the steps of receiving a plurality of texts, each of the plurality of texts comprising a plurality of syntactic units; extracting, for each of the plurality of syntactic units, at least one subject and/or at least one object; classifying, for each of the plurality of syntactic units, whether each of the plurality of syntactic units comprises a relation; extracting, for each of the plurality of syntactic units, based on the at least one subject, the at least one object, and the syntactic unit, a predicate; normalizing, for each of the plurality of syntactic units, the at least one subject and the at least one object, and appending identifiers to the at least one subject and the at least one object, respectively; and outputting, for each of the syntactic units, a triplet comprising the at least one subject, the at least one object, and the predicate.