USFD: Twitter NER with Drift Compensation and Linked Data

This paper describes a pilot NER system for Twitter, comprising the USFD system en-try 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 un-supervised 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 ex-amination of the target dataset in the context of this task.