Corina Dima

Reverse-engineering Language: A Study on the Semantic Compositionality of German Compounds

In this paper we analyze the performance of different composition models on a large dataset of German compound nouns. Given a vector space model for the German language, we try to reconstruct the observed representation (the corpusestimated vector) of a compound by composing the observed representations of its two immediate constituents. We explore the composition models proposed in the literature and also present a new, simple model that achieves the best performance on our dataset.

On the Compositionality and Semantic Interpretation of English Noun Compounds.

In this paper we present a study covering the creation of compositional distributional representations for English noun compounds (e.g. computer science) using two compositional models proposed in the literature. The compositional representations are first evaluated based on their similarity to the corresponding corpus-learned representations and then on the task of automatic classification of semantic relations for English noun compounds. Our experiments show that compositional models are able to build meaningful representations for more than half of the test set compounds. However, using pre-trained compositional models does not lead to the expected performance gains for the semantic relation classification task. Models using compositional representations have a similar performance as a basic classification model, despite the advantage of being pretrained on a large set of compounds.