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OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

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arxiv 1909.13078 v1 pith:4K2M436X submitted 2019-09-28 cs.CL

Xu Han , Tianyu Gao , Yuan Yao , Demin Ye , Zhiyuan Liu , Maosong Sun This is my paper · ORCID
classification cs.CL
keywords opennretoolkitextractionfactsmodelsonlinesystemvarious
verification ladder T0 review T1 audit T2 compute T3 formal
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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 http://github.com/thunlp/OpenNRE.

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  1. A survey on cutting-edge relation extraction techniques based on language models

    cs.CL 2024-11 conditional novelty 3.0 of 10

    A survey of 2020-2023 ACL-family papers on relation extraction finds BERT-based models dominate, while large language models like T5 show promise mainly in few-shot settings.

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