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.

本文提出使用多视角学习的方法，以提高知识库的实体类型信息的准确性和覆盖范围，通过研究不同的语言和表示方法的组合，实现了在维基百科上进行实体类型判断，提出了一个多语言、多视角的数据集MVET。

多重视角学习：多语言和多表示实体分类