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We describe the SemEval-2010 shared task on “Linking Events and Their Participants in Discourse”. This task is an extension to the classical semantic role labeling task. While semantic role labeling is traditionally viewed as a sentence-internal task, local semantic argument structures clearly interact with each other in a larger context, e.g., by sharing references to specific discourse entities or events. In the shared task we looked at one particular aspect of cross-sentence links between argument structures, namely linking locally uninstantiated roles to their co-referents in the wider discourse context (if such co-referents exist). This task is potentially beneficial for a number of NLP applications, such as information extraction, question answering or text summarization.
Reframing FrameNet Data
(2004)
The Berkeley FrameNet Project (http://www.icsi.berkeley.edu/~framenet) is building an on-line lexical resource for contemporary English. The database provides information about the semantic and syntactic combinatorial possibilities (valences) of each item analyzed. This paper describes the conceptual basis for what has been called reframing of data in the FrameNet database and exemplifies two new frame-to-frame relations, Causative_of and Inchoative_of, the implementation of which came about as a result of reanalysis of certain frames and lexical units. The new relations are characterized with respect to a triple of frames involving the notion of attaching, and entering them into the database is demonstrated using the Frame Relations Editor. The two relations allow FrameNet to make frame-wise distinctions that capture fairly systematic semantic relationships across sets of lexical units. While the Inheritance and Subframe relations are of particular interest to the NLP research community, Causative_of and Inchoative_of may be more relevant to lexicography.
The classification of verbs in Levin's (1993) English Verb Classes and Alternations: A preliminary Investigation, on the basis of both intuitive semantic grouping and their participation in valence alternations, is often used by the NLP community as evidence of the semantic similarity of verbs (Jing & McKeown 1998; Lapata & Brew 1999; Kohl et al. 1998). In this paper, we compare the Levin classification with the work of the FrameNet project (Fillmore & Baker 2001), where words (not just verbs) are grouped according to the conceptual structures (frames) that underlie them and their combinatorial patterns are inductively derived from corpus evidence. This means that verbs grouped together in FrameNet (FN) might be semantically similar but have different (or no) alternations, and that verbs which share the same alternation might be represented in two different semantic frames.