Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks,
eg, part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc …

Data augmentation is an effective solution to data scarcity in low-resource scenarios.
However, when applied to token-level tasks such as NER, data augmentation methods often …

Abstract Named Entity Recognition (NER) remains difficult in real-world settings; current
challenges include short texts (low context), emerging entities, and complex entities (eg …

Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great
improvements for many NLP tasks on a variety of languages. However, recent works also …

We take a step towards addressing the under-representation of the African continent in NLP
research by bringing together different stakeholders to create the first large, publicly …

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022
Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a …

Named entity recognition (NER) in a real-world setting remains challenging and is impacted
by factors like text genre, corpus quality, and data availability. NER models trained on …

Named entity recognition (NER) is an important task in the processing of natural language,
which needs to determine entity boundaries and classify them into pre-defined categories …

The disparity in the languages commonly studied in Natural Language Processing (NLP) is
typically reflected by referring to languages as low vs high-resourced. However, there is …

Complex Named Entity Recognition (NER) is the task of detecting linguistically complex
named entities in low-context text. In this paper, we present ACLM Attention-map aware …