Active Entity Recognition in Low Resource Settings

The task of Named Entity Recognition (NER) has been well studied under high-resource conditions (e.g., extracting named mentions of PERSON, ORGANIZATION and LOCATION from news articles). However, there are very few studies of the NER task for open-domain collections and in low-resource settings. We focus on NER for low-resource collections, in which any entity types of practical interest to the users of the system must be supported. We try to achieve this with a low cost of annotation of data from the target domain/collection. We propose an entity recognition framework that combines active learning and conditional random fields (CRF), and which provides the flexibility to define new entity types as needed by the users. Our experiments on a help & support corpus show that the system can achieve F1 measure of 0.77 by relying on only 100 manually-annotated sentences.