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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) adoi:10.18653/v1/d15-1083 dblp:conf/emnlp/YaghoobzadehS15 fatcat:uxr6mr74urac5apwtoxx6dk6om