Papers with real-time extraction
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population (2022.emnlp-demos)
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Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li
| Challenge: | Existing knowledge extraction tools are not complete due to emerging entities and relations in real-world applications. |
| Approach: | They propose an open-source knowledge extraction toolkit DeepKE that supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
| Outcome: | The proposed toolkit supports low-resource, document-level and multimodal scenarios in the knowledge base population. |
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction (D19-3)
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| Challenge: | OpenNRE provides a framework to implement neural relation extraction (RE) . the toolkit provides various functional modules based on TensorFlow and PyTorch . |
| Approach: | OpenNRE is an open-source framework to implement neural relation extraction models. they also release an online system to meet real-time extraction without any training and deployment. |
| Outcome: | OpenNRE provides a framework to implement neural models for relation extraction (RE) the toolkit also includes an online system to meet real-time extraction without training and deployment . |