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        <title>Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases</title>
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        <namePart type="given">Hailong</namePart>
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        <namePart type="given">Tiansi</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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    <abstract>Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0% and 5.2% improvement in Mi-F1 and Ma-F1, respectively.</abstract>
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%0 Conference Proceedings
%T Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases
%A Jin, Hailong
%A Hou, Lei
%A Li, Juanzi
%A Dong, Tiansi
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F jin-etal-2018-attributed
%X Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0% and 5.2% improvement in Mi-F1 and Ma-F1, respectively.
%U https://aclanthology.org/C18-1024
%P 282-292

Markdown (Informal)

[Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases](https://aclanthology.org/C18-1024) (Jin et al., COLING 2018)

• Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases (Jin et al., COLING 2018)

ACL

• Hailong Jin, Lei Hou, Juanzi Li, and Tiansi Dong. 2018. Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases. In Proceedings of the 27th International Conference on Computational Linguistics, pages 282–292, Santa Fe, New Mexico, USA. Association for Computational Linguistics.