Evaluating and combining named entity recognition systems

Abstract

Name entity recognition (NER) is an important subtask in natural language processing. Various NER systems have been developed in the last decade. They may target for different domains, employ different methodologies, work on different languages, detect different types of entities, and support different inputs and output formats. These conditions make it difficult for a user to select the right NER tools for a specific task. Motivated by the need of NER tools in our research work, we select several publicly available and well-established NER tools to validate their outputs against both Wikipedia gold standard corpus and a small set of manually annotated documents. All the evaluations show consistent results on the selected tools. Finally, we constructed a hybrid NER tool by combining the best performing tools for the domains of our interest.