OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

Abstract

OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE methods, OpenNRE not only allows developers to train custom models to extract structured relational facts from the plain text but also supports quick model validation for researchers. Besides, OpenNRE provides various functional RE modules based on both TensorFlow and PyTorch to maintain sufficient modularity and extensibility, making it becomes easy to incorporate new models into the framework. Besides the toolkit, we also release an online system to meet real-time extraction without any training and deploying. Meanwhile, the online system can extract facts in various scenarios as well as aligning the extracted facts to Wikidata, which may benefit various downstream knowledge-driven applications (e.g., information retrieval and question answering). More details of the toolkit and online system can be obtained from

Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors

“…The widely used New York Times dataset (Riedel et al, 2010) contains 53 relation categories including a negative relation (NA) indicating no relation between two entities. We use the version of the data provided by the OpenNRE framework (Han et al, 2019), which removes overlapping pairs between train and test data. The dataset statistics are shown in Table 1.…”

Neural relation extraction: a review

“…However, insufficient amount of training data is a major drawback as labeled data is not always available in real life scenarios. Table 1 shows total number of relations and sentences provided in common relation extraction datasets according to OpenNRE framework [20]. Table 1.…”

Towards Understanding Gender Bias in Relation Extraction

“…We evaluate OpenNRE (Han et al, 2019), a popular open-source NRE system. OpenNRE implements the approach from (Lin et al, 2016).…”

“…We define male (female) datapoints to be relations for which the head entity is male (female), which means the distantly supervised sentence is taken from a male (female) article. Prior work has used area under the precision-recall curve and F1 score to measure NRE model performance (Gupta et al, 2019;Han et al, 2019;Kuang et al, 2019). We use Macro-F1 score as our performance metric.…”