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        <title>Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction</title>
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    <name type="personal">
        <namePart type="given">Pankaj</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0% and 2.7% for entity recognition and relation classification, respectively on CoNLL04 dataset.</abstract>
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%0 Conference Proceedings
%T Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction
%A Gupta, Pankaj
%A Schütze, Hinrich
%A Andrassy, Bernt
%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 gupta-etal-2016-table
%X This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0% and 2.7% for entity recognition and relation classification, respectively on CoNLL04 dataset.
%U https://aclanthology.org/C16-1239
%P 2537-2547

Markdown (Informal)

[Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction](https://aclanthology.org/C16-1239) (Gupta et al., COLING 2016)

• Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction (Gupta et al., COLING 2016)

ACL

• Pankaj Gupta, Hinrich Schütze, and Bernt Andrassy. 2016. Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2537–2547, Osaka, Japan. The COLING 2016 Organizing Committee.