Microsoft Technology Licensing, LLC

Haoyong Zhang

Ming Zhou

Wei-Ying Ma

Jianfeng Gao

Gregory Nicholas Hullender

Lei Cui

Furu Wei

Jian H Li

Mu Li

Kun Jing

Xiaosong Yang

Kati Amanda London

Shaohan Huang

Janine Crumb

Shuang Peng

Elliott K Olds

Yu-Ting Kuo

Yajuan Lv

An example environment can automatically generate content for a conversation in response to a presented content item. An example computing device receives information associated with a content item produced by a source system, the content item being accessible to other the computing devices via a network. In response to receiving the information, the computing device identifies a start comment. In response to identifying the start comment, the computing device identifies a reply to the start comment using at least a ranking model.

Automatic conversation creator for news

Entity pages are created that are optimized for search engines to return entity information from the entity pages in response to search queries. An entity page may be created for an entity by identifying electronic content sources that include data about the entity. Usage data indicative of how users have consumed data at the electronic content sources is also determined. The usage data is analyzed to identify topics for the entity and topic content is retrieved from the electronic content sources to create topic summaries. An entity page with the topics summaries is generated. When a search engine receives a search query related to the entity, the search engine may provide information from the entity page in response to the search query.

Entity page generation and entity related searching

Candidate suggestions for correcting misspelled query terms input into a search application are automatically generated. A score for each candidate suggestion can be generated using a first decoding pass and paths through the suggestions can be ranked in a second decoding pass. Candidate suggestions can be generated based on typographical errors, phonetic mistakes and/or compounding mistakes. Furthermore, a ranking model can be developed to rank candidate suggestions to be presented to a user.

Query speller

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Redmond, WA, United States of America

Haoyong Zhang