A comparison of word similarity measures for noun compound disambiguation

Noun compounds occur frequently in many languages, and the problem of semantic disambiguation of these phrases has many potential applications in natural language processing and other areas. One very common approach to this problem is to define a set of semantic relations which capture the interaction between the modifier and the head noun, and then attempt to assign one of these semantic relations to each compound. For example, the compound phrase flu virus could be assigned the semantic relation causal (the virus causes the flu); the relation for desert wind could be location (the wind is located in the desert). In this paper we investigate methods for learning the correct semantic relation for a given noun compound by comparing the new compound to a training set of hand-tagged instances, using the similarity of the words in each compound. The main contribution of this paper is to directly compare distributional and knowledge-based word similarity measures for this task, using various datasets and corpora. We find that the knowledge based system provides a much better performance when adequate training data is available