Syntactic Features and Word Similarity for Supervised Metonymy Resolution

We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness

A Conceptual Reasoning Approach to Textual Ellipsis

We present a hybrid text understanding methodology for the resolution of textual ellipsis. It integrates conceptual criteria (based on the well-formedness and conceptual strength of role chains in a terminological knowledge base) and functional constraints reflecting the utterances' information structure (based on the distinction between context-bound and unbound discourse elements). The methodological framework for text ellipsis resolution is the centering model that has been adapted to these constraints.Comment: 5 pages, uuencoded gzipped PS file (see also Technical Report at: http://www.coling.uni-freiburg.de/public/papers/ecai96.ps.gz

Understanding metonymies in discourse

We propose a new computational model for the resolution of metonymies, a particular type of figurative language. Typically, metonymies are considered as a violation of semantic constraints (e.g., those expressed by selectional restrictions) that require some repair mechanism (e.g., type coercion) for proper interpretation. We reject this view, arguing that it misses out on the interpretation of a considerable number of utterances. Instead, we treat literal and figurative language on a par, by computing both kinds of interpretation independently from each other as long as their semantic representation structures are consistent with the underlying knowledge representation structures of the domain of discourse. The following general heuristic principles apply for making reasonable selections from the emerging readings. We argue that the embedding of utterances in a coherent discourse context is as important for recognizing and interpreting metonymic utterances as intrasentential semantic constraints. Therefore, in our approach, (metonymic or literal) interpretations that establish referential cohesion are preferred over ones that do not. In addition, metonymic interpretations that conform to a metonymy schema are preferred over metonymic ones that do not, and metonymic interpretations that are in conformance with knowledge-based aptness conditions are preferred over metonymic ones that are not. We lend further credit to our model by discussing empirical data from an evaluation study which highlights the importance of the discourse embedding of metonymy interpretation for both anaphora and metonymy resolution