A family of fuzzy orthogonal projection models for monolingual and cross-lingual hypernymy prediction

Hypernymy is a semantic relation, expressing the “is-a” relation between a concept and its instances. Such relations are building blocks for large-scale taxonomies, ontologies and knowledge graphs. Recently, much progress has been made for hypernymy prediction in English using textual patterns and/or distributional representations. However, applying such techniques to other languages is challenging due to the high language dependency of these methods and the lack of large training datasets of lower-resourced languages. In this work, we present a family of fuzzy orthogonal projection models for both monolingual and cross-lingual hypernymy prediction. For the monolingual task, we propose a Multi-Wahba Projection (MWP) model to distinguish hypernymy vs. non-hypernymy relations based on word embeddings. This model establishes distributional fuzzy mappings from embeddings of a term to those of its hypernyms and non-hypernyms, which consider the complicated linguistic regularities of these relations. For cross-lingual hypernymy prediction, a Transfer MWP (TMWP) model is proposed to transfer the semantic knowledge from the source language to target languages based on neural word translation. Additionally, an Iterative Transfer MWP (ITMWP) model is built upon TMWP, which augments the training sets of target languages when target languages are lower-resourced with limited training data. Experiments show i) MWP outperforms previous methods over two hypernymy prediction tasks for English; and ii) TMWP and ITMWP are effective to predict hypernymy over seven non-English languages.