Abstract: A method for entity extraction within an electronic document including executing by a computer processor a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model; the conditional random field algorithm having an input including one or more training text documents; executing by a computer processor an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model; the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm; applying by a computer processor the conditional random field model to at least one electronic document; wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity; applying by a computer processor the entity extraction model to the at least one electronic document; wherein application of the entity extraction model

Abstract: A method for entity extraction within an electronic document including executing by a computer processor a conditional random field algorithm stored on a computer readable medium to generate a conditional random field model; the conditional random field algorithm having an input including one or more training text documents; executing by a computer processor an entity extraction algorithm stored on a computer readable medium to generate an entity extraction model; the entity extraction algorithm having an input including the same one or more training text documents input into the conditional random field algorithm; applying by a computer processor the conditional random field model to at least one electronic document; wherein application of the conditional random field model returns a list of passages in the at least one electronic document having an entity; applying by a computer processor the entity extraction model to the at least one electronic document; wherein application of the entity extraction model

Abstract: A system and method for clustering electronic documents where the method includes identifying a plurality of electronic documents stored on a computer readable medium, determining by a computer processor a distance metric between each document in said plurality of electronic documents, and grouping by the computer processor one or more documents from said plurality of electronic documents into clusters based on a maximum permissible distance metric between documents within a cluster.

Abstract: The methods proposed here deconstructs training sentences into a stream of features that represent both the sentences and tokens used by the text, their sequence and other ancillary features extracted using natural language processing. Then, we use a conditional random field where we represent the concept we are looking for as state A and the background (everything not concept A) as a state B. The model created by this training phase is then used to locate the concept as a sequence of sentences within a document. This has distinct advantages in accuracy and speed over methods that individually classify each sentence and then use a secondary method to group the classified sentences into passages. Furthermore while previous methods were based on searching for the occurrence of tokens only, the use of a wider set of features enables this method to locate relevant passages even though a different terminology is in use.