  Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for
increasing accuracy and coverage of entity type information in KBs. We rely on
two metaviews: language and representation. For language, we consider
high-resource and low-resource languages from Wikipedia. For representation, we
consider representations based on the context distribution of the entity (i.e.,
on its embedding), on the entity's name (i.e., on its surface form) and on its
description in Wikipedia. The two metaviews language and representation can be
freely combined: each pair of language and representation (e.g., German
embedding, English description, Spanish name) is a distinct view. Our
experiments on entity typing with fine-grained classes demonstrate the
effectiveness of multiview learning. We release MVET, a large multiview - and,
in particular, multilingual - entity typing dataset we created. Mono- and
multilingual fine-grained entity typing systems can be evaluated on this
dataset.

Computation and Language;Artificial Intelligence