Abstract: An overwhelming number of articles are available everyday via the internet. Unfortunately, it is impossible to peruse more than a handful, and it is difficult to ascertain an article's social context. The techniques disclosed herein address this problem by harnessing implicit and explicit contextual information from social media. By extracting text surrounding a hyperlink to an article in a post and assessing the article as a function of content surrounding the hyperlink, an article's social context is determined and presented. Additionally, articles that are sufficiently similar in content may be grouped to establish a many-to-one relationship between posts and an article, creating a more accurate assessment.

Abstract: A training system is described for generating at least one ranking module using features derived, in part, from region information. The region information encodes characteristics about regions which are associated with queries in search log data. A query processing system is also described for applying the ranking model generated by the training system to process queries in real time. In one implementation, the training system can also generate plural ranking models corresponding to plural respective map areas. The training system can also generate a mapping model which correlates each region with a ranking model to be applied when processing queries that originate from that region. The query processing system can process a query by determining a region associated with the query and then identifying and applying a ranking model which corresponds to the region.

Abstract: An overwhelming number of articles are available everyday via the internet. Unfortunately, it is impossible to peruse more than a handful, and it is difficult to ascertain an article's social context. The techniques disclosed herein address this problem by harnessing implicit and explicit contextual information from social media. By extracting text surrounding a hyperlink to an article in a post and assessing the article as a function of content surrounding the hyperlink, an article's social context is determined and presented. Additionally, articles that are sufficiently similar in content may be grouped to establish a many-to-one relationship between posts and an article, creating a more accurate assessment.

Abstract: Index structures and query processing framework that enforces a given threshold on the overhead of computing conjunctive keyword queries. This includes a keyword processing algorithm, logic to determine which indexes to materialize, and a probabilistic approach to reducing the overhead for determining which indexes to build. The index structures leverage the fact that the frequency distribution of natural-language text follows a power law. Given a document collection, a set of indexes is proposed for materialization so that the time for intersecting keywords does not exceed a given threshold ?. When considering the associated space requirement, the additional indexes are limited. Materialization of such a set of indexes for reasonable values of ? (e.g., the time required to scan 20% of the largest inverted index), at least for a collection of short documents is distributed by the power law.