US 2015/0310096 A1Published application

Comparing document contents using a constructed topic model

AssigneeInternational Business Machines Corporation

InventorsShenghua Bao, Hong Lei Guo, Zhi Li Guo, Davide Pasetto, Wei Hong Qian, Zhong Su

Life of this patent

Estimating the term…

Loading patent…

Abstract

Comparing document contents is provided. An ontological concept is extracted from a text snippet of a corpus document. One or more feature vectors are constructed that include associative information that describes an ontology that includes the focused concept. A topic model is trained using the one or more feature vectors. First and second topic sets are respectively extracted from first and second documents using the topic model. One or more topics from the first topic set are matched, using the topic model, with one or more topics from the second topic set to construct a matched topic set. Semantic analyses are respectively performed on first and second text snippet sets, wherein the first and second text snippet sets are chosen based, at least in part, on the matched topic set. Text snippets are matched based, at least in part, on the first and second semantic analyses.

Claims20

Dependent claims

A method comprising:

extracting, by one or more computer processors, a focused concept from a text snippet of a corpus document;
constructing, by one or more computer processors, one or more feature vectors for the focused concept, wherein each feature vector includes associative information that describes an ontology that includes the focused concept;
training a topic model, by one or more computer processors, based, at least in part, on at least one of the one or more feature vectors;
responsive to extracting, by one or more computer processors, a first topic set from a first document and a second topic set from a second document, matching, by one or more computer processors, one or more topics from the first topic set with one or more topics from the second topic set, using the topic model, to construct a matched topic set;
and
responsive to performing, by one or more computer processors, a first semantic analysis on a first text snippet set of the first document and a second semantic analysis on a second text snippet set of the second document, wherein the first and the second text snippet sets are chosen based, at least in part, on the matched topic set, matching, by one or more computer processors, one or more text snippets of the first text snippet set with one or more text snippets of the second text snippet set based, at least in part, on the first and the second semantic analyses.

3 more elements

A non-transitory computer program product comprising:

a computer readable storage medium and program instructions stored on the computer readable storage medium, the program instructions comprising:
program instructions to extract a focused concept from a text snippet of a corpus document;
program instructions to construct one or more feature vectors for the focused concept, wherein each feature vector includes associative information that describes an ontology that includes the focused concept;
program instructions to train a topic model based, at least in part, on at least one of the one or more feature vectors;
program instructions to, responsive to program instructions to extract a first topic set from a first document and a second topic set from a second document, match, using the topic model, one or more topics from the first topic set with one or more topics from the second topic set to construct a matched topic set;
and
program instructions to, responsive to program instructions to perform a first semantic analysis on a first text snippet set of the first document and a second semantic analysis on a second text snippet set of the second document, wherein the first and the second text snippet sets are chosen based, at least in part, on the matched topic set, match one or more text snippets of the first text snippet set with one or more text snippets of the second text snippet set based, at least in part, on the first and the second semantic analyses.

4 more elements

A computer system comprising:

one or more computer processors;
one or more computer readable storage media;
program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising:
program instructions to extract a focused concept from a text snippet of a corpus document;
program instructions to construct one or more feature vectors for the focused concept, wherein each feature vector includes associative information that describes an ontology that includes the focused concept;
program instructions to train a topic model based, at least in part, on at least one of the one or more feature vectors;
program instructions to, responsive to program instructions to extract a first topic set from a first document and a second topic set from a second document, match, using the topic model, one or more topics from the first topic set with one or more topics from the second topic set to construct a matched topic set;
and
program instructions to, responsive to program instructions to perform a first semantic analysis on a first text snippet set of the first document and a second semantic analysis on a second text snippet set of the second document, wherein the first and the second text snippet sets are chosen based, at least in part, on the matched topic set, match one or more text snippets of the first text snippet set with one or more text snippets of the second text snippet set based, at least in part, on the first and the second semantic analyses.

6 more elements

Dependencies are inferred from the claim text.