Scalable, explainable, adaptive information extraction from structure-aware nearest neighbor

Information extraction (IE) is a crucial task in natural language processing, focusing on classifying and structuring informative elements from textual data. Despite significant performance improvements in recent years, existing methods face challenges in terms of scalability, adaptability, and explainability when applied in real-world scenarios. This paper presents a pioneering approach, namely the KNN-IE framework, tailored to address this challenge in key IE tasks. Diverging from conventional KNN methods primarily employed in classification tasks based on point-to-point similarity, our framework introduce an instance-to-instance similarity, called structural similarity, where each instance can contain multiple points as its elements. To provide supervision signals during training and enable better prediction explanations, we define a text- based measurement between two instances which is reasonable and can be calculated before trainingIn the training phase, we leverage structure-aware contrastive learning to help model learn structural similarity between different instances, especially those with the same type. During inference, schema-constraint bipartite matching between target text and retrieved instances enables KNN-IE to concurrently tackle the classification and structuring of informative elements, in a single round of retrieval process. Every time after finishing extraction, those retrieved structurally similar instances can work as well comprehensible evidences to help user understand why KNN-IE makes such predictions. Experimental evaluations across four prevalent IE tasks and ten diverse datasets demonstrate that KNN-IE can compete with state-of-the-art IE methods in fully supervised settings. Other results further underscore its superior scalability, adaptability, and explainability, affirming its potential for effective deployment in practical applications.