Abstract: In recent decades,natural language processing has made great progress. Better model of each sub-problem achieves 90 0 o accuracy or better. However, success in integrated, end-to-end natural language understanding remains elusive. I}he main reasons arc that the systems processing sensory input typically have a pipeline architecture: the output of each stage is the input of the next, and errors are cascaded and accumulated through the pipeline of naively chained components, and there is no feedback from later stages to earlier ones. Actually, later stages can help earlier ones to process.Now a number of researchers have paid attention to this problem and proposed some joint approaches. But they do not perform open information extraction (Open IE)，which can identify various types of relations without requiring prespecifications. We proposed a joint inference model, which is based on Markov logic and can perform both traditional relation extraction and open IE. The proposed modeling significantly outperforms the other open IE systems in terms of both precision and recall. The joint inference is efficient and we have demonstrated its efficacy in real-world open IE detection tasks.

Key words: Oven information cxtraction.Markov logic nctworks.loint inference