We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality.While previous embeddings have shown limitations on prototypical hypernyms, HyperVec represents an unsupervised measure where embeddings are learned in a specific order and capture the hypernym-hyponym distributional hierarchy.Moreover, our model is able to generalize over unseen hypernymy pairs, when using only small sets of training data, and by mapping to other languages.Results on benchmark datasets show that HyperVec outperforms both state-of-theart unsupervised measures and embedding models on hypernymy detection and directionality, and on predicting graded lexical entailment.

This paper provides a binary, token-based classification of German particle verbs (PVs) into literal vs. non-literal usage.A random forest improving standard features (e.g., bagof-words; affective ratings) with PV-specific information and abstraction over common nouns significantly outperforms the majority baseline.In addition, PV-specific classification experiments demonstrate the role of shared particle semantics and semantically related base verbs in PV meaning shifts.