Hewlett-packard Development Company, L.p.

Sheng-Wen Yang

Yuhong Xiong

Wei Liu

Suk Hwan Lim

Nina Bhatti

Jian-Ming Jin

Parag Mulendra Joshi

Samson J Liu

Li Zhang

Jian Fan

Jerry J Liu

Ping Luo

Ren Wu

Bao-Yao Zhou

A computer-implemented method of extracting key phrases from a document is disclosed comprising the steps of accessing a repository comprising linked subjects, the repository comprising first and second data structures representing the relationship between said subjects using different representation criteria; pruning the first data structure by removing links between subjects based on a further relationship between said subjects in the second data structure; matching phrases in said document to subjects in the pruned first data structure; further pruning the pruned first data structure by removing unmatched subjects that are not linked to matched subjects; determining a ranking for each matched subject; and selecting key phrases using the determined subject rankings. A computer program for implementing the steps of this method when executed on a computer is also disclosed.

Document key phrase extraction method

Semantically ranking content in a website () with a computerized ranking device () includes: parsing content from the website () into multiple autonomous content blocks (-to-) with the computerized ranking device () and assigning an importance ranking with said computerized ranking device () to each of the content blocks (-to-) based on a degree to which a substance of the content block (-to-) is relevant to one of a plurality of predefined categories.

Semantically ranking content in a website

A system and method for automatically extracting meta data from unstructured electronic documents is disclosed. In one embodiment, the unstructured electronic document is converted into a plain text document. Further, a document header of the unstructured electronic document is extracted from the plain text document using a rule-based document header extractor, where the rule-based document header extractor may be based on a rule that includes determining a ratio of a number of words with their initial letters capitalized in a text line over a total number of words in the text line in the plain text document. Moreover, meta data is extracted from the extracted document header using a heuristic approach.

System and method for automatically extracting metadata from unstructured electronic documents

A device for classifying a document comprises a module to generate a data tree structure and configured to assign terms to a first plurality of nodes of the data tree structure, where each of the first plurality of nodes is assigned a weight. In assigning the weights of the first plurality of nodes, a first generation of combinations of possible weights assignable as the weights of the first plurality of nodes is obtained, and a second generation of combinations of possible weights assignable as the weights of the first plurality of nodes is obtained by performing the genetic algorithms in the first generation of combinations of possible weights. The device determines whether the document is in a document class based at least the weights of the first plurality of nodes.

Classification of a document according to a weighted search tree created by genetic algorithms

A method of grouping a plurality of media content is provided. The method includes converting at least a portion of the media content into at least one document object model ('DOM') using a processor. The DOM can include a plurality of block elements, each comprising at least one content object. The method includes apportioning the content objects into a relevant portion and an irrelevant portion and extracting a set of keywords, the set comprising at least one keyword, within the relevant portion of the content objects. The method includes apportioning the relevant portion of the content objects into a related portion and an unrelated portion using at least a portion of the set of keywords and grouping the related portion of the content to provide a group of related content.

Content grouping systems and methods

Growing community of inventors

Beijing, China

Sheng-Wen Yang