Paper: Encoding Relation Requirements for Relation Extraction via Joint Inference

ACL ID P14-1077
Title Encoding Relation Requirements for Relation Extraction via Joint Inference
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Most existing relation extraction models make predictions for each entity pair lo- cally and individually, while ignoring im- plicit global clues available in the knowl- edge base, sometimes leading to conflicts among local predictions from different en- tity pairs. In this paper, we propose a joint inference framework that utilizes these global clues to resolve disagree- ments among local predictions. We ex- ploit two kinds of clues to generate con- straints which can capture the implicit type and cardinality requirements of a relation. Experimental results on three datasets, in both English and Chinese, show that our framework outperforms the state-of-the- art relation extraction models when such clues are applicable to the datasets. And, we find that the clues learnt automatically from ...