Abstract
The task of end-to-end relation extraction consists of two sub-tasks: (i) identifying entity mentions along with their types and (ii) recognizing semantic relations among the entity mention pairs. It has been shown that for better performance, it is necessary to address these two sub-tasks jointly [13, 22]. We propose an approach for simultaneous extraction of entity mentions and relations in a sentence, by using inference in Markov Logic Networks (MLN) [21]. We learn three different classifiers: (i) local entity classifier, (ii) local relation classifier and (iii) “pipeline” relation classifier which uses predictions of the local entity classifier. Predictions of these classifiers may be inconsistent with each other. We represent these predictions along with some domain knowledge using weighted first-order logic rules in an MLN and perform joint inference over the MLN to obtain a global output with minimum inconsistencies. Experiments on the ACE (Automatic Content Extraction) 2004 dataset demonstrate that our approach of joint extraction using MLNs outperforms the baselines of individual classifiers. Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.
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Notes
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We consider the “head” extent of a mention defined by ACE standard as the entity mention so that all the valid entity mentions are always non-overlapping.
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All the rules can’t be listed because of the space constraints.
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We have not yet acquired a more recent ACE 2005 dataset.
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Pawar, S., Bhattacharya, P., Palshikar, G.K. (2018). End-to-End Relation Extraction Using Markov Logic Networks. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_41
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