Abstract: Systems and methods can identify potential entities from facts generated from web-based sources. For example, a method may include generating a source data graph for a potential entity from a text document in which the potential entity is identified. The source data graph represents the potential entity and facts about the potential entity from the text document. The method may also include clustering a plurality of source data graphs, each for a different text document, by entity name and type, wherein at least one cluster includes the potential entity. The method may also include verifying the potential entity using the cluster by corroborating at least a quantity of determinative facts about the potential entity and storing the potential entity and the facts about the potential entity, wherein each stored fact has at least one associated text document.

Abstract: Systems and methods can identify potential entities from facts generated from web-based sources. For example, a method may include generating a source data graph for a potential entity from a text document in which the potential entity is identified. The source data graph represents the potential entity and facts about the potential entity from the text document. The method may also include clustering a plurality of source data graphs, each for a different text document, by entity name and type, wherein at least one cluster includes the potential entity. The method may also include verifying the potential entity using the cluster by corroborating at least a quantity of determinative facts about the potential entity and storing the potential entity and the facts about the potential entity, wherein each stored fact has at least one associated text document.

Abstract: A method for sequence tagging medical patient records includes providing a labeled corpus of sentences taken from a set of medical records, initializing generative parameters ? and discriminative parameters {tilde over (?)}, providing a functional LL?C×Penalty, where LL is a log-likelihood function LL = log ? ? p ? ( ? , ? ~ ) + ? l = 1 M ? ? [ log ? ? p ? ( X l , Y l | ? ~ ) - log ? ? p ? ( X l | ? ~ ) ] + ? l = 1 M ? ? log ? ? p ? ( X l | ? ) , ? Penalty = ? y ? V Y ? ( em y 2 + tr y 2 + e ? ? m ~ y 2 + t ? ? r ~ y 2 ) , where emy=1???xi?VXp(xi|y), e{tilde over (m)}y=1???xi?VX{tilde over (p)}(xi|y) are emission probability constraints, try=1???yi?VYp(yi|y), t{tilde over (r)}y=1???yi?VY{tilde over (p)}(yi|y) are transition probability constraints, and extracting gradients of LL?C×Penalty with respect to the transition and emission probabilities and solving ?k*,{tilde o

Abstract: A method for sequence tagging medical patient records includes providing a labeled corpus of sentences taken from a set of medical records, initializing generative parameters ? and discriminative parameters {tilde over (?)}, providing a functional LL?C×Penalty, where LL is a log-likelihood function LL = log ? ? p ? ( ? , ? ~ ) + ? l = 1 M ? ? [ log ? ? p ? ( X l , Y l | ? ~ ) - log ? ? p ? ( X l | ? ~ ) ] + ? l = 1 M ? ? log ? ? p ? ( X l | ? ) , ? Penalty = ? y ? V Y ? ( em y 2 + tr y 2 + e ? ? m ~ y 2 + t ? ? r ~ y 2 ) , where emy=1???xj?VXp(xi|y), e{tilde over (m)}y=1???xi?VX{tilde over (p)}(xi|y) are emission probability constraints, try=1???yi?VYp(yi|y), t{tilde over (r)}y=1???yi?VY{tilde over (p)}(yi|y) are transition probability constraints, and extracting gradients of LL?C×Penalty with respect to the transition and emission probabilities and solving ?*k,{tilde o