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        <title>Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing</title>
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    <name type="personal">
        <namePart type="given">Yadollah</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="family">Tsujii</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1343</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
%A Yaghoobzadeh, Yadollah
%A Schütze, Hinrich
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yaghoobzadeh-schutze-2018-multi
%X 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.
%R 10.18653/v1/D18-1343
%U https://aclanthology.org/D18-1343
%U https://doi.org/10.18653/v1/D18-1343
%P 3060-3066

Markdown (Informal)

[Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing](https://aclanthology.org/D18-1343) (Yaghoobzadeh & Schütze, EMNLP 2018)

• Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing (Yaghoobzadeh & Schütze, EMNLP 2018)

ACL

• Yadollah Yaghoobzadeh and Hinrich Schütze. 2018. Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3060–3066, Brussels, Belgium. Association for Computational Linguistics.