Using WordNet to automatically deduce relations between words in noun-noun compounds
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
The role of experience in the interpretation of noun---noun combinations
Artificial Intelligence Review
Supporting creative product/commercial design with computer-based image retrieval
Proceedings of the 14th European conference on Cognitive ergonomics: invent! explore!
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How do people understand noun---noun compounds such as volcano science and pear bowl? In this paper, we present evidence against one approach to noun---noun compounds, namely that of arranging the meanings of compounds into a small, finite taxonomy of general semantic relations. Using a typical relation taxonomy, we conducted an experiment examining how people classify compounds into the taxonomy's relation categories. We found that people often select not one but several relations for each compound; for example, people classify coffee stain as coffee MAKES stain, stain MADE OF coffee, coffee CAUSES stain and stain DERIVED FROM coffee. A natural metric for relational similarity follows from our experimental data; we found that using cluster analysis to group compounds' interpretations with respect to this metric produced groupings that were different from the original taxonomic categories, suggesting that there is more than one way to classify the meanings of compounds. We also found that compounds which had similar constituent concepts tended to be interpreted with similar relations, indicating that the intrinsic properties of a compound's constituent concepts help determine how that compound is interpreted. Such findings are problematic for taxonomic theories of conceptual combination