Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction

• URL: http://arxiv.org/abs/2103.11062v1
• Date: Sat, 20 Mar 2021 00:16:29 GMT
• Title: Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
• Authors: Kareem Ahmed, Eric Wang, Guy Van den Broeck, Kai-Wei Chang
• Abstract summary: We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Our approach employs semantic loss which captures the precise meaning of a logical sentence. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.
• Score: 54.06292969184476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Such knowledge takes the form of an ontology defining relations and their permissible arguments. Previous approaches set out to integrate such knowledge in their learning approaches either through self-training, or through approximations that lose the precise meaning of the logical expressions. By contrast, our approach employs semantic loss which captures the precise meaning of a logical sentence through maintaining a probability distribution over all possible states, and guiding the model to solutions which minimize any constraint violations. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.

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