Microsoft Technology Licensing, LLC

Pavel Dmitriev

Ryen William White

Ovidiu Dan

Wei Zhuang

Ambarish Chitnis

Lin Tang

Timothy Edgar

Rajanikanth Ageeru

An entity summarization system is described herein that mines the Internet and other data source to provide answers to questions such as the relative sentiment of users towards various brands. The system uses a controlled vocabulary list describing a specific aspect of entities of interest. Given an entity name, the system scans the whole content corpus to collect statistics on the words that occur most frequently in the context of the entity name, taking into account proximity information, to produce a weighted list of vocabulary terms describing the entity. Two entities can be compared by normalizing and comparing their weighted term lists. In some embodiments, the system performs these procedures efficiently by leveraging an N-gram web model. Thus, the system provides an automated way to compare two entities to derive information about how users feel about the entities at any given time.

Entity summarization and comparison

Embodiment described herein are generally directed to a toolbar extension of a web browser that grabs a user's search engine query and suggests a refined search query known to yield better search results. The toolbar recognizes the web page the user is on as being associated with a search engine and retrieves the user's search query. The toolbar interacts with a refinement component on a server, and the refinement component determines a refined search query based on confidence scores assigned to data mined from a data center affiliated with different search engine (one related to the toolbar). The refined search query is returned and displayed in a search field of the toolbar, allowing the user to easily run the refined search on the different search engine.

Query refinement in a browser toolbar

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Bellevue, WA, United States of America

Pavel Dmitriev