Rembrandt is a named entity recognition system specially crafted to annotate documents by classifying named entities and ground them into unique identifiers. Rembrandt played an important role within our research over geographic IR, thus evolving into a more capable framework where documents can be annotated, manually curated and indexed. The goal of this paper is to present Rembrandt's simple but powerful annotation framework to the NLP community.

In this paper we provide an overview of the first evaluation contest for named entity recognition in Portuguese, HAREM, which features several original traits and provided the first state of the art for the field in Portuguese, as well as a public-domain evaluation architecture.