Abstract: System, method, apparatus, and program instruction for comparing an input query clause with a collection of document clauses to determine which document clause is most similar to the query clause is provided. The disclosed invention includes an improved process of storing a collection of natural language data, improving both the generalizability and accuracy of searching and comparison of natural language data.

Abstract: Semantic frame identification involves associating identified target words in the sentential context of their natural language source with semantic frames from a frame lexical database. The disclosed invention utilizes a transformer for semantic frame identification of a target word in a natural language input. Through the transformer's multi-headed attention mechanism, the model learns contextual relationships between words in an input text. The disclosed invention generates a list of potential substitute words for a target word and then verifies the context and meaning of the words in the list to identify a frame from a frame lexical database.

Abstract: Named Entity Recognition is the identification and classification of named entities within a document. Nested NEs occur when an NE is contained within another NE. The disclosed invention leverages the CapsNet architecture for improved nested NE identification and classification. This includes deriving the features of an input text. The derived features are used to identify and classify any named entities in the text. The system is further configured to identify named entities in the text and perform clustering to group named entities. The disclosed CapsNet considers the context of the whole text to activate higher capsule layers in order to identify the named entities and classify them. The teachings of this invention are applicable to other NER models to improve nested NE identification and classification.

Abstract: Named Entity Disambiguation is the process of identifying unique entities within a document. The disclosed invention leverages the CapsNet architecture for improved NED, which in the preferred embodiment includes NER. This is done by deriving the features of an input text, which are used to identify, classify, and disambiguate any named entities in the text. The system is further configured to identify named entities in the text and perform clustering to group named entities. Named entities are disambiguated to identify which named entity the text refers to uniquely. The disclosed CapsNet considers the context of the whole text to activate higher capsule layers in order to identify, classify, and disambiguate named entities.

Abstract: Named Entity Recognition is the identification and classification of named entities within a document. The disclosed invention leverages the CapsNet architecture for improved NE identification and classification. This includes deriving the features of an input text. The derived features are used to identify and classify any named entities in the text. The system is further configured to identify named entities in the text and perform clustering to group named entities. The disclosed CapsNet considers the context of the whole text to activate higher capsule layers in order to identify named entities and classify them.

Abstract: Semantic frame identification involves associating identified target words in the sentential context of their natural language source with semantic frames from a frame lexical database. The disclosed invention leverages the CapsNet architecture for improved semantic frame identification of a target word in a natural language input. This includes deriving the features of a target word identified in the sentence and extracting the features of the word units and the thematic words around the target word. Through dynamic routing of capsules, the CapsNet is able to filter the candidate frames for the target word to reduce the search space and apply the CapsNet prediction to identify a frame from a frame lexical database.

Abstract: A computer-based method for modeling scenarios for clinical trials in a drug development process is disclosed. The method provides study data to a computer system where the study data represents data from one or more studies on patients performed during the clinical trials of the drug. The method displays on a display device of the computer system the study data for one or more studies and displays scenarios on the display device where the scenarios represent changes to be made to the one or more studies to model the effect of the changes on the clinical trials. The system allows changing one or more of the parameters for the one or more studies using the scenarios displayed to model the effect of the changes on the clinical trials. Then the effect of the changes on the clinical trials effectuated by changing the one or more parameters is displayed.