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- W2891881175 abstract "Accurate and complete knowledge bases (KBs) are paramount in NLP. We employ mul-itiview learning for increasing the 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." @default.
- W2891881175 created "2018-09-27" @default.
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- W2891881175 date "2018-01-01" @default.
- W2891881175 modified "2023-10-16" @default.
- W2891881175 title "Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing" @default.
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- W2891881175 doi "https://doi.org/10.18653/v1/d18-1343" @default.
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