Abstract: A method for extracting semantic hashtags representing topics in one or more domain-specific documents, each topic relevant to achieving a goal of a domain-specific entity includes a processor executing a routine to split a domain-specific document into data objects, the data objects comprising sentences and paragraphs, using grammar rules specific to the domain-specific entity; applying an unsupervised learning model to classify the data objects as noisy and non-noisy for the domain-specific entity; discarding the noisy data objects; applying a supervised learning model to identify, based on a pre-defined set of intents, an intent of each non-noisy data object; tagging each non-noisy data object with its intent; applying the intent to an ontology graph base to identify a corresponding semantic hashtag; annotating each non-noisy data object with its semantic hashtag; and using one or more annotated non-noisy data objects, generating, for the domain-specific entity, a recommended action for achieving the goal.

Abstract: A computerized method for extracting domain specific insights from a corpus of files containing large documents comprising: breaking down large chunks of text into smaller sentences/short paragraphs in a domain specific way, identifying and removing domain noise; identifying the sentence intents of the non-noise sentences; tagging the sentences with other domain specific attributes; defining a semantic ontology using a graph database based on the sentence intents, a multitude of mini-dictionaries and domain attributes; applying a pre-defined ontology to tag documents with domain specific hashtags; and combining the hashtags using machine learning techniques into insights.

Abstract: A computerized method for extracting domain specific insights from a corpus of files containing large documents comprising: breaking down large chunks of text into smaller sentences/short paragraphs in a domain specific way, identifying and removing domain noise; identifying the sentence intents of the non-noise sentences; tagging the sentences with other domain specific attributes; defining a semantic ontology using a graph database based on the sentence intents, a multitude of mini-dictionaries and domain attributes; applying a pre-defined ontology to tag documents with domain specific hashtags; and combining the hashtags using machine learning techniques into insights.