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        <title>End-to-end Relation Extraction using Neural Networks and Markov Logic Networks</title>
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        <namePart type="given">Sachin</namePart>
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    <abstract>End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Traditionally, separate predictive models were trained for each of these tasks and were used in a “pipeline” fashion where output of one model is fed as input to another. But it was observed that addressing some of these tasks jointly results in better performance. We propose a single, joint neural network based model to carry out all the three tasks of boundary identification, entity type classification and relation type classification. This model is referred to as “All Word Pairs” model (AWP-NN) as it assigns an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. We also propose to refine output of the AWP-NN model by using inference in Markov Logic Networks (MLN) so that additional domain knowledge can be effectively incorporated. We demonstrate effectiveness of our approach by achieving better end-to-end relation extraction performance than all 4 previous joint modelling approaches, on the standard dataset of ACE 2004.</abstract>
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%0 Conference Proceedings
%T End-to-end Relation Extraction using Neural Networks and Markov Logic Networks
%A Pawar, Sachin
%A Bhattacharyya, Pushpak
%A Palshikar, Girish
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F pawar-etal-2017-end
%X End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions. Traditionally, separate predictive models were trained for each of these tasks and were used in a “pipeline” fashion where output of one model is fed as input to another. But it was observed that addressing some of these tasks jointly results in better performance. We propose a single, joint neural network based model to carry out all the three tasks of boundary identification, entity type classification and relation type classification. This model is referred to as “All Word Pairs” model (AWP-NN) as it assigns an appropriate label to each word pair in a given sentence for performing end-to-end relation extraction. We also propose to refine output of the AWP-NN model by using inference in Markov Logic Networks (MLN) so that additional domain knowledge can be effectively incorporated. We demonstrate effectiveness of our approach by achieving better end-to-end relation extraction performance than all 4 previous joint modelling approaches, on the standard dataset of ACE 2004.
%U https://aclanthology.org/E17-1077
%P 818-827

Markdown (Informal)

[End-to-end Relation Extraction using Neural Networks and Markov Logic Networks](https://aclanthology.org/E17-1077) (Pawar et al., EACL 2017)

• End-to-end Relation Extraction using Neural Networks and Markov Logic Networks (Pawar et al., EACL 2017)

ACL

• Sachin Pawar, Pushpak Bhattacharyya, and Girish Palshikar. 2017. End-to-end Relation Extraction using Neural Networks and Markov Logic Networks. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 818–827, Valencia, Spain. Association for Computational Linguistics.