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Bingfeng Luo

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AAAI Conference 2020 Conference Paper

Integrating Relation Constraints with Neural Relation Extractors

Yuan Ye
Yansong Feng
Bingfeng Luo
Yuxuan Lai
Dongyan Zhao

Recent years have seen rapid progress in identifying prede- ﬁned relationship between entity pairs using neural networks (NNs). However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often deﬁned over combinations of entity-relation-entity triples, since there often lack of explicitly well-deﬁned type and cardinality requirements for the relations. In this paper, we propose a uniﬁed framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. Particularly, we develop two efﬁcient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction (NRE) models even enhanced with extra post-processing. Our source code and datasets will be released at https: //github. com/PKUYeYuan/ Constraint-Loss-AAAI-2020.

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AIJ Journal 2018 Journal Article

Encoding implicit relation requirements for relation extraction: A joint inference approach

Liwei Chen
Yansong Feng
Songfang Huang
Bingfeng Luo
Dongyan Zhao

Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on. Most existing relation extractors make predictions for each entity pair locally and individually, while ignoring implicit global clues available across different entity pairs and in the knowledge base, which often leads to conflicts among local predictions from different entity pairs. This paper proposes a joint inference framework that employs such global clues to resolve disagreements among local predictions. We exploit two kinds of clues to generate constraints which can capture the implicit type and cardinality requirements of a relation. Those constraints can be examined in either hard style or soft style, both of which can be effectively explored in an integer linear program formulation. Experimental results on both English and Chinese datasets show that our proposed framework can effectively utilize those two categories of global clues and resolve the disagreements among local predictions, thus improve various relation extractors when such clues are applicable to the datasets. Our experiments also indicate that the clues learnt automatically from existing knowledge bases perform comparably to or better than those refined by human.

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