  This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We propose FIGMENT to tackle this problem. FIGMENT is
embedding-based and combines (i) a global model that scores based on aggregated
contextual information of an entity and (ii) a context model that first scores
the individual occurrences of an entity and then aggregates the scores. In our
evaluation, FIGMENT strongly outperforms an approach to entity typing that
relies on relations obtained by an open information extraction system.
