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.
@article{arxiv.1511.03088,
title = {USFD: Twitter NER with Drift Compensation and Linked Data},
author = {Leon Derczynski and Isabelle Augenstein and Kalina Bontcheva},
journal= {arXiv preprint arXiv:1511.03088},
year = {2015}
}
Comments
Paper in ACL anthology: https://aclweb.org/anthology/W/W15/W15-4306.bib