Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present invention relates to novel compounds of Formula (I), wherein M, A and Y are defined as in Formula (I); invention compounds are modulators of metabotropic glutamate receptors—subtype 4 (“mGluR4”) which are useful for the treatment or prevention of central nervous system disorders as well as other disorders modulated by mGluR4 receptors. The invention is also directed to pharmaceutical compositions and the use of such compounds in the manufacture of medicaments, as well as to the use of such compounds for the prevention and treatment of such diseases in which mGluR4 is involved.

Abstract: 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.

Abstract: 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.