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        <title>Joint Learning of Local and Global Features for Entity Linking via Neural Networks</title>
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    <name type="personal">
        <namePart type="given">Thien</namePart>
        <namePart type="given">Huu</namePart>
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    <name type="personal">
        <namePart type="given">Nicolas</namePart>
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        <namePart type="given">Mariano</namePart>
        <namePart type="family">Rodriguez Muro</namePart>
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    <name type="personal">
        <namePart type="given">Oktie</namePart>
        <namePart type="family">Hassanzadeh</namePart>
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    <name type="personal">
        <namePart type="given">Alfio</namePart>
        <namePart type="family">Massimiliano Gliozzo</namePart>
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        <namePart type="given">Mohammad</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.</abstract>
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%0 Conference Proceedings
%T Joint Learning of Local and Global Features for Entity Linking via Neural Networks
%A Nguyen, Thien Huu
%A Fauceglia, Nicolas
%A Rodriguez Muro, Mariano
%A Hassanzadeh, Oktie
%A Massimiliano Gliozzo, Alfio
%A Sadoghi, Mohammad
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F nguyen-etal-2016-joint
%X Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.
%U https://aclanthology.org/C16-1218
%P 2310-2320

Markdown (Informal)

[Joint Learning of Local and Global Features for Entity Linking via Neural Networks](https://aclanthology.org/C16-1218) (Nguyen et al., COLING 2016)

• Joint Learning of Local and Global Features for Entity Linking via Neural Networks (Nguyen et al., COLING 2016)

ACL

• Thien Huu Nguyen, Nicolas Fauceglia, Mariano Rodriguez Muro, Oktie Hassanzadeh, Alfio Massimiliano Gliozzo, and Mohammad Sadoghi. 2016. Joint Learning of Local and Global Features for Entity Linking via Neural Networks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2310–2320, Osaka, Japan. The COLING 2016 Organizing Committee.