This task consists of recognizing words and phrases that evoke semantic frames of the sort defined in the FrameNet project (http://framenet.icsi.berkeley.edu), and their semantic dependents, which are usually, but not always, their syntactic dependents (including subjects). For example, in the sentence Matilde said, "I rarely eat rutabaga.", said evokes the Statement frame, and eat evokes the Ingestion frame. The role of Speaker in the Statement frame is filled by Matilda, and the role of Message, by the whole quotation. In the Ingestion frame,I is the Ingestor and rutabaga fills the Ingestibles role. Since the Ingestion event is contained within the Message of the Statement event, we can represent the fact that the Message conveyed was about Ingestion, just by annotating the sentence with respect to these two frames. Note that these role names (called Frame Elements, or FEs) are quite frame-specific; generalizations over them, relating most of them to a small set of thematic roles are expressed via frame-to-frame and FE-FE relations. A number of systems have been built to automatically label the frame-evoking words with appropriate frames (similar to WSD) and their dependents with FE names.

The training data will consist of FrameNet data release 1.3, containing more than 150,000 manually annotated instances of frames. The testing data will consist of several previously unannotated texts from the American National Corpus (http://www.americannationalcorpus.org); the gold standard will consist of manual annotations (by the FrameNet team) of these texts for all frame evoking expressions and the fillers of the associated frame elements. The evaluation will measure precision and recall for frames and frame elements, with partial credit for incorrect but closely related frames. This is a more advanced version of the Automatic Semantic Role Labeling task of Senseval-3 (Litkowski 2004).