Full natural language understanding requires identifying and analyzing the meanings of metaphors, which are ubiquitous in both text and speech. Over the last thirty years, linguistic metaphors have been shown to be based on more general conceptual metaphors, partial semantic mappings between disparate conceptual domains. Though some achievements have been made in identifying linguistic metaphors over the last decade or so, little work has been done to date on automatically identifying conceptual metaphors. This paper describes research on identifying conceptual metaphors based on corpus data. Our method uses as little background knowledge as possible, to ease transfer to new languages and to mini- mize any bias introduced by the knowledge base construction process. The method relies on general heuristics for identifying linguistic metaphors and statistical clustering (guided by Wordnet) to form conceptual metaphor candidates. Human experiments show the system effectively finds meaningful conceptual metaphors.

Online news sites often include a comments section where readers are allowed to leave their thoughts. These comments often contain interesting and insightful conversations between readers about the news article. However the richness of these conversations is often lost among other meaningless comments, and moreover all comments are found at the bottom of the web page. In this article, we discuss how our system inserts reader conversations into the news article to create a multimedia presentation called Shout Out. Shout Out features two virtual news anchors: one anchor reads the news and when appropriate the anchor pauses to have a conversation about the news with another anchor. This current iteration of Shout Out combines natural language techniques and reader conversations to create an engaging system.