Rogers Communications Inc.

Liqin Xu

Hyun Chul Lee

Yingbo Miao

Ke Zeng

Profiling systems and methods of creating and using user interest profiles are described. In some example embodiments, the method includes: creating a topic set which includes topics which are organized in a hierarchical structure which includes a plurality of topic levels including an upper topic level and a lower topic level, each topic in the lower topic level being a subtopic of at least one of the topics in the upper topic level; monitoring interest in a plurality of documents for a user to identify one or more documents-of-interest to the user; and based on the monitored interest for the user, creating an interest profile for the user by determining a measure of topical interest for the user for at least one of the topics at the upper topic level and for a subtopic of that topic, the subtopic being at the lower topic level.

Systems and methods for creating an interest profile for a user

Methods and comment association systems for associating one or more comments with one or more primary electronic documents are described. In one aspect, the method comprises: identifying, at a comment association system, one or more key terms from at least a portion of the one or more primary electronic documents; identifying, at the comment association system, one or more comments associated with the identified key terms; determining, at the comment association system, whether an identified comment is sufficiently related to the one or more primary electronic documents by calculating one or more relation score for that identified comment and comparing the relation score to one or more threshold; and if the identified comment is sufficiently related to the one or more primary electronic documents, then associating the identified comment with the one or more primary electronic documents at the comment association system.

System and method for matching comment data to text data

A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase.

System and method for phrase identification

A method and system for cleaning an electronic document are provided. The method comprises: identifying at least one sentence in the electronic document; numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence; inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and removing sentences determined to be bad sentences from the electronic document to create a cleaned document.

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Markham, Canada

Liqin Xu