Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun Compounds

Factoring Ambiguity out of the Prediction of Compositionality for German Multi-Word Expressions

Ambiguity represents an obstacle for distributional semantic models(DSMs), which typically subsume the contexts of all word senses within one vector. While individual vector space approaches have been concerned with sens…

The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective

Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds

Predicting the degree of compositionality of noun compounds such as {``}snowball{''} and {``}butterfly{''} is a crucial ingredient for lexicography and Natural Language Processing applications, to know whether the compou…

Measuring Compositionality in Representation Learning

Many machine learning algorithms represent input data with vector embeddings or discrete codes. When inputs exhibit compositional structure (e.g. objects built from parts or procedures from subroutines), it is natural to…

Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality

In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information a…