The LinGO Grammar Matrix is a computational foundation for implementing grammars of natural languages in the HPSG framework. In order to provide students and researchers wishing to build such grammars with a head start, the Matrix customization system presents the user with a typological questionnaire via a web page, then based on the answers, creates a limited but functional starter grammar. Recent work has focused on expanding the questionnaire to handle a variety of case systems, and to allow the description of inflectional marking on nouns, verbs, and determiners.
While typical news-aggregation sites do a good job of clustering news stories according to topic, they leave the reader without information about which stories figure prominently in political discourse. BLEWS uses political blogs to categorize news stories according to their reception in the conservative and liberal blogospheres. It visualizes information about which stories are linked to from conservative and liberal blogs, and it indicates the level of emotional charge in the discussion of the news story or topic at hand in both political camps. BLEWS also offers a "see the view from the other side" functionality, enabling a reader to compare different views on the same story from different sides of the political spectrum. BLEWS achieves this goal by digesting and analyzing a real-time feed of political-blog posts provided by the Live Labs Social Media platform, adding both link analysis and text analysis of the blog posts.
The Microsoft Research ESL Assistant is a web service that provides correction suggestions for typical ESL (English as a Second Language) errors. Such errors include, for example, the choice of determiners (the/a) and the choice of prepositions. The web service also provides word choice suggestions from a thesaurus. In order to help the user make decisions on whether to accept a suggestion, the service displays "before and after" web search results so that the user can see real-life examples of the usage of both their original input and the suggested correction. Error detection and correction are based on machine-learned and heuristic modules, combined with a large language model.