The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The disambiguation of nominalizations
Computational Linguistics
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Annotating and learning compound noun semantics
ACL '07 Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop
Co-occurrence contexts for noun compound interpretation
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
New Regularized Algorithms for Transductive Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
SemEval-2010 task 9: the interpretation of noun compounds using paraphrasing verbs and prepositions
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
SemEval-2010 task 9: The interpretation of noun compounds using paraphrasing verbs and prepositions
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
UCD-PN: Selecting general paraphrases using conditional probability
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
UvT: Memory-based pairwise ranking of paraphrasing verbs
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Automatic interpretation of noun compounds using wordnet similarity
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Search and mining entity-relationship data
Proceedings of the 20th ACM international conference on Information and knowledge management
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A noun compound (NC) is a sequence of two or more nouns (entities) acting as a single noun entity that encodes implicit semantic relation between its noun constituents. Given an NC such as 'headache pills' and possible paraphrases such as: 'pills that induce headache' or 'pills that relieve head-ache' can we learn to choose which verb: 'induce' or 'relieve' that best describes the semantic relation encoded in 'headache pills'? In this paper, we describe our approaches to rank human-proposed paraphrasing verbs of NCs. Our contribution is a novel approach that uses two-step process of clustering similar NCs and then labeling the best paraphrasing verb as the most prototypical verb in the cluster. The approach performs the best with an average Spearman's rank correlation of 0.55. This approach, while being computationally simpler, gives a better ranking than the current state of the art. The result shows the potential of our approach for finding implicit relations between entities especially when the relations are not explicit in the context in which the entities appear, rather they are implicit in the relationship between its constituents.