Increasing NER Recall with Minimal Precision Loss

Named Entity Recognition (NER) is broadly used as a first step toward the interpretation of text documents. However, for many applications, such as forensic investigation, recall is currently inadequate, leading to loss of potentially important information. Entity class ambiguity cannot be resolved reliably due to the lack of context information or the exploitation thereof. Consequently, entity classification introduces too many errors, leading to severe omissions in answers to forensic queries. We propose a technique based on multiple candidate labels, effectively postponing decisions for entity classification to query time. Entity resolution exploits user feedback: a user is only asked for feedback on entities relevant to his/her query. Moreover, giving feedback can be stopped anytime when query results are considered good enough. We propose several interaction strategies that obtain increased recall with little loss in precision.