Abstract: A method of creating action-trigger phrase sets includes receiving a document from a corpus of documents; processing text from the document; and creating an action-trigger phrase set from the text.

Abstract: An approach to information retrieval is contemplated for facilitating semantic search and response over a large domain of technical documents is disclosed. First, the grammar and morphology of the statements and instructions expressed in the technical documents is used to filter training data to extract the text that is most information-rich, that is the text that contains domain-specific jargon, in context. This training data is then vectorized and fed as input to an SBERT neural network model that learns an embedding of related words and terms in the text, i.e. the relationship between a given set of words contained in a user's query and the instructions from the technical documentation text most likely to assist in the user's operations. There are two parsing tasks. The first is to select a minimal sample of sentences from the document corpus that capture the domain-specific terminology (jargon). The result is set of sentences used to train BERT and SBERT.

Abstract: A method of parsing implicit tables within a document includes parsing, by a processor, a document to determine a pattern for a set of text that sets apart the information contained therein from the rest of the text; determining, by the processor, from the pattern of spacing, a first descriptive phrase and a second descriptive phrase; and assigning, by the processor, the first descriptive phrase as an action and the second descriptive phrase as a trigger.

Abstract: A method of creating a trained database from a document corpus includes creating a tailored neural network for the document corpus by using sentences to create word-level association and sentence-level association; and applying action-trigger phrase sets, created front the document corpus independent of creating the tailored neural network, to the tailored neural network to create a trained database; wherein processing a query through the trained database, rather than the document corpus, increases the speed of processing the query and increases the accuracy of the result.