LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition

TL;DR

This paper presents a Transformer-based system for English complex named entity recognition, achieving competitive results in SemEval-2022, and explores data augmentation with entity linking which did not improve performance.

Contribution

The paper introduces a simple Transformer-based baseline for complex NER and evaluates data augmentation with entity linking in this context.

Findings

Achieved a macro F1 score of 72.50% on the test set.

Ranked 12th out of 30 teams in the leaderboard.

Data augmentation with entity linking did not improve performance.

Abstract

Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro F1 score of 72.50% on the held-out test set. We have also explored a data augmentation approach using entity linking. While the approach does not improve the final performance, we also discuss it in this paper.