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        <title>Context-Aware Representations for Knowledge Base Relation Extraction</title>
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        <namePart type="given">Daniil</namePart>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at https://github.com/ukplab/.</abstract>
    <identifier type="citekey">sorokin-gurevych-2017-context</identifier>
    <identifier type="doi">10.18653/v1/D17-1188</identifier>
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%0 Conference Proceedings
%T Context-Aware Representations for Knowledge Base Relation Extraction
%A Sorokin, Daniil
%A Gurevych, Iryna
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F sorokin-gurevych-2017-context
%X We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at https://github.com/ukplab/.
%R 10.18653/v1/D17-1188
%U https://aclanthology.org/D17-1188/
%U https://doi.org/10.18653/v1/D17-1188
%P 1784-1789

Markdown (Informal)

[Context-Aware Representations for Knowledge Base Relation Extraction](https://aclanthology.org/D17-1188/) (Sorokin & Gurevych, EMNLP 2017)

• Context-Aware Representations for Knowledge Base Relation Extraction (Sorokin & Gurevych, EMNLP 2017)

ACL

• Daniil Sorokin and Iryna Gurevych. 2017. Context-Aware Representations for Knowledge Base Relation Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1784–1789, Copenhagen, Denmark. Association for Computational Linguistics.