  This paper describes a pilot NER system for Twitter, comprising the USFD
system entry to the W-NUT 2015 NER shared task. The goal is to correctly label
entities in a tweet dataset, using an inventory of ten types. We employ
structured learning, drawing on gazetteers taken from Linked Data, and on
unsupervised clustering features, and attempting to compensate for stylistic
and topic drift - a key challenge in social media text. Our result is
competitive; we provide an analysis of the components of our methodology, and
an examination of the target dataset in the context of this task.
