Patent Document ID: 8930304
Application ID: 14154236

Base Claim:
1. A method for characterizing a set of documents, comprising: identifying a network of multilevel hierarchically related documents having direct and indirect references associated with content relationships; for each respective document, determining a set of latent topic characteristics captured by a Bernoulli process, based on at least both an intrinsic content of the respective document and a set of latent topic characteristics based on a respective content of other documents which are directly referenced and indirectly referenced through at least one other document to the respective document, such that a topic distribution of each respective document is a mixture of distributions associated with at least the at least one other document; representing a set of latent topics for the respective document based on a joint probability distribution of at least the latent topic characteristics based on the intrinsic content and the respective content of other documents which are directly referenced and indirectly referenced through at least one other document to the respective document, dependent on the identified network and a random process; and storing, in a memory, the represented set of latent topics for the respective document.

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Claim 5:
5. The method according to claim 1 , wherein the iterative process at a reference level comprises iterating, for each document d j , for the i-th location in document d j , choosing a topic z ji from the topic distribution of document d j , p(z|d j ,θ d j ), where the distribution parameter θ d j is drawn from a Dirichlet distribution Dir(α), choosing a word w ji which follows the multinomial distribution p(w|z ji ,Λ) conditioned on the topic z ji , and incrementing the locations and documents, wherein the iterative process at a document level comprises iterating, for each document d s , for the i-th location in document d s , choosing a referenced document c si from p(c|d s ,Ξ), a multinomial distribution conditioned on the document d s , choosing a topic t si from the topic distribution of the document c si at the reference level, and choosing a word w si which follows the multinomial distribution p(w|t si ,Λ) conditioned on the topic t si , where Ξ is a mixing coefficient matrix which represents how much of the content of the respective document is from direct or indirect references, and a composition of Ξ and θ models the topic distribution at the document level, and incrementing the locations and documents, wherein a number of latent topics is K and the mixing coefficients are parameterized by an N×N matrix Ξ where Ξ js =p(c si =d j |d s ), which are treated as a fixed quantity computed from the reference information of the set of documents, wherein topic distributions at the reference level are parameterized by a K×N matrix Θ where Θ lj =p(z ji =l|d j ), which is to be estimated, and an M×K word probability matrix Λ, where Λ hl =p(w si h =1|t si =l), which is to be estimated, and wherein the references comprise citations, each document d s having a set of citations Q d s , further comprising constructing a matrix S to denote direct relationships among the documents wherein S ls = 1  Q d s  for d l εQ d s and 0 otherwise, where |Q d s | denotes the size of the set Q d s , and employing a generative process for generating a related document c from the respective document d s , comprising: setting l=s; choosing t˜Bernoulli(β); if t=1, choosing h˜Multinomial(S .,l ), where S .,l denotes the l-th column; setting l=h, and returning to said choosing step; and if t=0, letting c=d l , to thereby combine a Bernoulli process and a random walk on a directed graph together, where a transitive property of the citations is captured, wherein the parameter β of the Bernoulli process determines a probability that the random walk stops at a current node, and the parameter β also specifies how much of the content of the respective document is influenced from the direct or indirect citations.