# USFD: Twitter NER with Drift Compensation and Linked Data

arXiv: https://arxiv.org/abs/1511.03088 · HTML: https://mlsanity.com/paper/1511.03088/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp](https://mlsanity.com/paper/2104.09742/) (2021)
- [TransDrift: Modeling Word-Embedding Drift using Transformer](https://mlsanity.com/paper/2206.08081/) (2022)

## Abstract (arXiv)

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.