NERD: Evaluating Named Entity Recognition Tools in the Web of Data

Abstract. The Web of data promotes the idea that more and more data are interconnected. A step towards this goal is to bring more structured annotations to existing documents using common vocabularies or ontolo-gies. Semi-structured texts such as scientific, medical or news articles as well as forum and archived mailing list threads or (micro-)blog posts can hence be semantically annotated. Named Entity (NE) extractors play a key role for extracting structured information by identifying features, also called entities, and by linking them to other web resources by means of typed inferences. In this article, we propose a thorough evaluation of five popular Linked Data entity extractors which expose APIs: Alche-myAPI, DBPedia Spotlight, Extractiv, OpenCalais and Zemanta. We present NERD, an evaluation framework we have developed and the re-sults of a controlled evaluation performed by human beings that consists in assigning a Boolean value to three criteria: entity detection, entity type and entity disambiguation